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Contents. Protecting Privacy. 27 Integrating with Enterprise Systems. 30 Handling Real Time Analytics. 34 Leveraging Cloud Computing.
- Dora Edwards
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2 Contents Introduction. 1 View Point Phil Shelley, CTO, Sears Holdings Making it Real Industry Use Cases Retail Extreme Personalization. 6 Airlines Smart Pricing. 9 Auto Warranty and Insurance Efficiency. 12 Financial Services Fraud Detection. 16 Energy Tapping Intelligence in Smart Grid / Meters. 19 Data warehousing Faster and Cost effective View Point Doug Cutting, Co-founder, Apache Hadoop Making it Real Key Challenges Protecting Privacy. 27 Integrating with Enterprise Systems. 30 Handling Real Time Analytics. 34 Leveraging Cloud Computing. 37 View Point S. Gopalakrishnan (Kris), Co-Chairman, Infosys Making it Real Infosys Adoption Enablers Accelerators Solution and Expertise. 43 Services Extreme Data. 46 Product Voice of Customer Analytics. 51 Platform Social Edge for Big Data. 53
3 What is Big Data? Introduction Today we live in the digital world. With increased digitization the amount of structured and unstructured data being created and stored is exploding. The data is being generated from various sources - transactions, social media, sensors, digital images, videos, audios and clickstreams for domains including healthcare, retail, energy and utilities. In addition to business and organizations, individuals contribute to the data volume. For instance, 30 billion content are being shared on Facebook every month; the photos viewed every 16 seconds in Picasa could cover a football field. It gets more interesting. IDC terms this as the Digital Universe and predicts that this digital universe is set to explode to an unimaginable 8 Zeta bytes by the year This would roughly be a stack of DVD s from Earth all the way to Mars. The term Big Data was coined to address this massive volume of data storage and processing. Clickstream Data Transaction Data Social Media Data Big Data (Volume, Velocity, Variety) Sensor Data Location/Geo Data It is increasingly becoming imperative for organizations to mine this data to stay Competitive. Analyzing data can provide significant competitive advantage for an enterprise. The data when analyzed properly leads to a wealth of information which helps the businesses to redefine strategies. However the current volume of big data sets are too complicated to be managed and processed by conventional relational databases & data warehousing technologies. The volume, variety and velocity of Big Data causes performance problems when being created, managed and analyzed using the conventional data processing techniques. Using conventional techniques for Big Data storage and analysis is less efficient as memory access is slower. The data collection is also challenging as the volume and variety of data has to be derived from sources of different types. The other major challenge in using the existing techniques is they require high end hardware to handle the data with a huge volume, velocity and variety. Big Data is a relatively new phenomenon. As with any new adoption, the adoption of Big Data depends on the tangible benefits it provides to Business. Large data sets which are considered as information overload are invariably treasure troves for business insights. The volume of data sets has immense value that can improve the business forecast, help in decision making, deciding business strategies over the competitors. For instance, Facebook, blogs and twitter data gives insights on current business trends. 1
4 Data Mining Faster Business Decisions Reports Real Time Analytics Forecasting Aggregated Intelligence Growth of Information Assets Innovative Business Value Storage The data sets are beyond the capability of humans to analyze manually. Big data tools have the ability to run ad-hoc queries against the large data sets in less time with a reasonable performance. For instance, in retail domain understanding what makes a buyer to look into a product online, sentiment analysis of a product based on the Facebook, tweet and blogs are of great value to the business. This will enable the business to improve their services for customers. Big Data analysis enables the executives to get the relevant data in less time for making decisions. Big Data can pave way for fraudulent analysis, customer segmentation based on the store behavior analysis, loyalty programs that identifies and targets the customers. This enables us to perform innovative analysis which indeed changes the way we think about data. Exploring Big Data Spectrum With unstructured data dominating the world of data, the way to exploit it is just becoming clearer. Information proliferation is playing a vital role in leveraging the opportunities and is also presenting a plethora of challenges. The industry opportunities presented by the plethora of data are plenty. To understand how to leverage Big Data opportunities is a clear need to the business.big Data spectrum covers use case from five different industries Retail, Airlines, Auto, Financial Services and Energy. All opportunities come with a set of challenges. The way to know and address these challenges is discussed in the Key Challenges section. To name a few: Data Privacy, 2
5 Data Security, Integrating various technologies, catering to real time flow of data and leveraging cloud computing. To dive deep into Big Data technology with the goal of having a quick, managed and quality implementation, a set of enablers were designed by the Architects at Infosys. The section on Adoption Enablers gives the insights into these enablers. The sections are interleaved with the viewpoints from Phil Shelly, CTO, Sears Holdings Corporation, Doug Cutting, Co-founder of Apache Hadoop (popularly known as father of Big Data) and Kris Gopalakrishnan, Co-Chairman, Infosys Ltd. 3
6 Phil Shelley CTO, Sears Holdings Corporation Dr. Shelley is a member of CIO forum, Big Data Chicago forum Phil, Sears is one of early adopter of Big Data. What are the sweet spot use cases in the retail industry? Transactional data such as POS, Web-based activity, loyalty-based activity, product push, seasonality, weather patterns and major trends that affect retail, business value that can be mined from this data. In addition to this you add the data in the social space and the sheer amount of this data is way beyond what traditional database solutions can handle. That is where Hadoop plays a role, to capture and keep this data in the finest level of detail. What kind of challenges are you facing in implementing Big Data solutions? Hadoop is relatively low cost to implement. However, to get started, you still need some kind of business case. It is a good idea to start small and have a very specific use case in mind. At the same time, a picture of where big data will be valuable long term is important as well. Focusing on a key use case that can demonstrate business value is probably the way to start, for any company a big-bang approach is not something I would recommend. How Big Data management integrates with your Enterprise systems? Hadoop is not a panacea. Big data solutions will be a hybrid of traditional databases, data warehouse appliances and Hadoop. The combination of high-speed SQL access and the heavy lifting of Hadoop can work together very well. This means that you need to synchronize the data between these data sources. One way to do this is to aggregate your data before you send it to an appliance. What data needs to be shared and how the synchronization happens will need some careful thinking. At least for the next few years it s going to be an ecosystem of Hadoop combined with a more traditional database system. How to handle Data Privacy and Security issues in the Big Data management? Personally I would not put very sensitive data on a public cloud because the risk of exposure could be catastrophic to the company. A private cloud which is co-located with my data center or, a virtual private cloud that is physically caged, are approaches I would recommend. Out of the box Hadoop has security limitations. You have to explicitly design your data for security. There are ways of securing credit card and 4
7 personal information. I would recommend that anyone looking to secure such data look to some help on how to structure a big data solution and not expose themselves. This is an area that is somewhat new and prone to lax security. Phil, One last question, as a CTO of Sears how do you see the connection between real-time Digital Enterprise and Big Data? Today Hadoop can have near real time copies of transactional data and near real time batch reporting with as little as minutes of latency. You can process the data near real time and then access from a data mart in real time, which create many possibilities. But it is really going to be an ecosystem of the right tools for the right jobs. 5
8 Making it Real Industry Use Cases Retail Extreme Personalization Use case Context There was a complete directory of the whole World Web in the beginning. One knew about all the servers that were in existence. Later other web directories appeared ex: Yahoo, AltaVista etc. which kept a hierarchy of the web pages based on their topics. In 1998 Google changed everything. Crawling, Indexing and Searching paved the way for Personalization and Clickstream analysis, by which personalized recommendations based on social, geographical and navigation could be made. This personal flavor gives higher value and causes customers to become more loyal and more profitable for businesses. Now, the channels by which voice of customer can be collected have multiplied leading to the big data explosion, and retailers have a huge opportunity to present customers with even more personalized promotions, deals and recommendations. Challenges One of the significant challenges in architecting such a personalization system is the amount and diversity of data that has to be handled. For example, websites today generate user activity data that could easily run into Terabytes in a matter of months. Equally problematic is the different formats and system interfaces. Once the data is loaded, the system applies correlation techniques to correlate the data and draw inferences about the preferences of individual customers. The traditional relational data warehousing OLAP based systems struggle to process this massive amount of high velocity data and provide insights. There is a high latency between the user s shopping activity and the generation of recommendation as well as limited granularity which decreases the relevance of the recommendation to the end customer. Solution and Architecture The personalization System sources information about the customer profile, orders, preferences and opinion from multiple sources some within the enterprise and some outside. Examples of sources are the enterprise order management system (or channel specific order management systems where applicable), customer browse analytics data from the website, opinions and reviews from social networks, etc. In addition to customer data, some master data like the sales catalog and marketing campaigns are loaded. There are 3 basic levels of inferences that can be done about what a consumer might be interested in: 1. Based on past actions and opinions. 2. Based on similar users (actual friends as defined by the consumer in social networks, statistically derived segments based on behavior). 3. Based on general public behavior 6
9 Transaction data from all channels Marketing Campaigns Data User profile data Master and channel specific catalogs Reviews/ ratings Web Analytics Social Networks Twitter Transformation Mapping Load Distributed and unified file system and map reduce processing infrastructure Disiributed Cache management Navigation related services Product context based services Promotion related services Rest API infrastructure Website Application StoresPOS Phone / Catalog sales Inferences from the personalization system can be consumed by multiple scenarios and systems. A website can use that to show content that is more relevant to the customer. The store systems can provide more relevant suggestions and customer service representatives might know more about the consumers than the consumers themselves! A personalization system has to process the data from various sources mentioned above and come up with personalized recommendations and business intelligence for individual customers. This is not a simple task. This involves processing tera/ peta bytes of data obtained from various sources. Traditional systems are not efficient and scalable in storing and processing this much amount of data. Big data processing solutions are the need for such a system. Typical Architecture of a Big Data processing system using Apache Hadoop Stack is shown below. In the context of extreme personalization, we can use the data extraction tools to load the data into a Hadoop system and use Mahout which is a scalable machine learning library which comes with a lot of algorithms for pattern mining, collaborative filtering and recommendations. The latest trend is the evolution of real-time systems like HStreaming and Twitter Storm which perform the analysis in an almost instant fashion compared to Hadoop which is run in batch mode. 7
10 Big Data Input Smart Data Output Business Txn data Log/Sensor information User generated Social Data Apache Hadoop Cluster Data Extraction Orchestration Mapreduce based Processing Flume Zookeeper Mahout Sqoop Oozie Hive Recommendation, Pattern AdhocQuery Results Analysis Output Other unstructured data Chukwa Azkaban Pig Data Summary Storage HBase HDFS Business Value Real-time Personalized insights: By combining inputs from various channels (social, location, history etc.) and analyzing them real-time, customers can be presented with almost instant recommendations. For example, if a customer tweets I like Xbox, the system can provide recommendations related to Xbox when she logs into the ecommerce site or as an ad on her social network profile or even send an Xbox promotion coupon to her mobile if she s shopping in-store. This kind of highly personalized and instant recommendation is being experimented and will become more prevalent going forward. Personalized Presentation Filtering: One of the fundamental things that can be offered is the ability to present content which is tuned to the preferences of the consumer. This could be in terms of product types (Wii related vs. Xbox related or full sleeve vs. half sleeve), brands (Sony vs. Samsung) or Prices (costliest vs. cheapest) or something else that we know about her. This can be provided as filtered navigation in a website or as a suggestive selling tip to a customer service representative while they are speaking to the consumer. Context-specific and Personalized External Content Aggregation: Presentation of context-specific information that makes sense for the consumer is a key capability. A good example is the relevance of Social context. If we are showing a product and can show the consumer that out of the 1500 people who said that they liked the product 15 are friends (with the capability to know who those 15 were as well), the impact and relevance of that would be significant. This service is relevant only for electronic channels. Personalized Promotional Content: Different consumers get attracted by different value propositions. Some like direct price cuts, some like more for the same money 8
11 (please note that they are exactly not the same), while some others believe in getting more loyalty points. Showing the most appropriate promotion/offer based on their interests is another important capability that a personalized system can provide. Big data processing solutions can process vast amount of data (tera/peta bytes) from various sources like browsing history, social network behavior, brand loyality, general public opinion about the product obtained from various social networks etc. This kind of extremely useful and tailor-made information can only be obtained using Big Data processing solutions and retailers must leverage them to make their business more appealing and personal to customers. Airlines Smart Pricing Use Case Context Air transportation is one of the toughest and most dynamic industries in the world. Constantly troubled by factors like oil prices, thin profit margins, environmental concerns, employee agitations etc, it is truly a world of survival of the fittest. Understanding the end consumer needs and discovering the competitive and attractive price to consumer is always a challenge. Airlines strive to achieve key differentiators like providing a personalized experience, enhance the brand, predictive intelligent systems for identifying profit and loss factors etc. In the airline industry, a few high-value customers can generate much more revenue than a number of low-value customers, which highlights the importance of CRM systems. Some of the questions faced by the airlines in relation to CRM include: How are the customers segmented? What share of profits does each customer segment bring in? Some customers deserve greater attention than others. How to identify them from the frequent flyers? What tactics should be adopted to acquire, convert, retain and engage customers? Personalization and Fare processing involves large data sets. While the current systems leverage conventional enterprise information, new data sources such as social media, web logs, call center logs & competitor pricings would have to be considered for personalization requirements. Here the challenges include highly scattered data sources, huge effort for data integration, and high cost of data warehousing and storage solutions. Scenario 1: Fare Processing: Context and Challenges Airlines classify fares into the main classes of service (Economy, Business, First etc.), and each of these are subdivided into booking classes. The number of booking classes 9
12 depends on factors like aircraft type, sector of the flight, flight date etc. The objective here is to have a greater level of control over the type of fares sold. When making a fare change, the fare is re-priced, which means recalculating each fare, taking into consideration factors like: Flown data / passenger booking information Forecasted data - load factor information Inventory Currency exchange rate at different points of sale Information about these factors will be distributed across various databases. Naturally, an attempt to process these data will run into all the above mentioned problems. And these data sources may not be sufficient to discover a price aligned to the customer s point of view. Opportunity Big Data for Fare Processing: Fare processing involves extracting and consolidating information from external sources such as Agent systems, External Pricing systems, and internal systems such as Revenue Accounting, Forecasting, Inventory and Yield Management systems. This data itself will run into tens of terabytes. Newer data sources such as Social Media conversations about pricing decisions & competitors, and un-structured data in enterprise like customer service data, call center logs etc. will push the data volumes even further. In this context, the high-cost data warehousing solutions are confronted with the following challenges. Expensive hardware and software : Cost grows with data size Analytics process needs to be 100% customizable, and not be governed by the confines of SQL Analytics process should not get extended by the size of the data; terabytes should be processed in minutes rather than days or hours Data loss is unacceptable, the solution requires high availability and failover Learning curve should not be steep Scenarios 2: Personalization for high-value customers Context and Challenges The airline industry is one where a few high value customers are more significant than many low value ones. Naturally, the winners are those who can do predictive analytics on the data and provide a personalized flying experience. A wealth of customer behavioral data can be gleaned from websites like route and class preferences, frequency of flying, baggage, dining information, geo-location etc. - to name a few. 10
13 This type of analytics requires accumulation of data from multiple sources, frequent processing of high volume data, flexibility and agility in the processing logic. These are all pain areas for traditional data warehouse solutions and these get compounded as the data volume grows. Opportunity Holistic data analytics and newer data Analyzing the customer s data from various systems including Loyalty, CRM, and Sales & Marketing etc. and data from partners systems along with data from social media based on customer social profile can help airlines to create deeper personalization preference of customers and also understand their current social status and preferences. Solution and Architecture Big Data Architecture leveraging Hadoop stack provides ability to extract, aggregate, load and process large volume of data in a distributed manner which in turn reduce the complexity and overall turn-around time in processing large volume of data. Proposed solution is aimed at primarily addressing the following challenges: Ability to manage and pre-process large volume of enterprise data, partners data and logs data and transform these data into meaningful data for analytics (Analysis, Preprocessing for Fare calculation and personalization services) Run analytics on un-structured data from social and other sources to derive newer dimension such as sentiment, buzz-words, root causes from customer interactions and user created content Data extracts from various feeder systems are copied to the Big Data file system. The Analytics platform will trigger the processing of this data, while responding to CRM Analyst Pricing Analyst Personalization data sources Fares & Pricing data sources Revenue Accounting Departure Control Sales& Marketing Loyalty Program Social Media CRM Tool Ticketing Price & Yield Mgmt. Reservation system Personalization Analytics CRM Datamart Fare Analytics Fare Datamart HDFS Cluster Job Pig Scheduler Hive Map reduce Big Data Ecosystem Data Data Gathering Gathering Big Data Architecture Personalization & Fare Processing RES Consolidator (ATPCO) Revenue Accounting system Fares DB External pricing Systems Online Agents GDS 11
14 information needs for various analytical scenarios. The Analytics platforms contain the algorithms tailored to the business process, to extract/mine meaningful information out of the raw data. Business Value By switching over to a Big Data based solution, it is estimated that the fare processing times can be cut down from days to a matter of hours or even minutes. The airline industry operates in a high risk domain vulnerable to a large number of factors, and this agility will be a vital tool in maximizing revenues. Big data architecture solutions for personalization helps in understanding and deriving granular personalization parameters and understand customers social status and needs based on their interaction in social media systems. The potential benefits for an airline could include:- Increased revenue & profit better fare management efficient mechanisms to handle fare changes Faster decision making and reduced time-to-market for key fares Introduction of competitive fares & reactive fare response Increased efficiencies to the airline in managing fare strategy for different sales channels Deeper understanding of customers and personalization (N=1) Auto Warranty & Insurance Efficiency Use case context Two of the biggest threats to the auto insurance companies are, economic downturn, which is making money unavailable, and other one is, insurance frauds. An insurance claim starts with the process of applying for a policy and setting up the right premium and then finishes with identifying the valid claims and minimize the frauds. The investigation done during the premium setting can help in minimizing the losses which can occur during the claim settlement to a great extent. These days there are lot of tools available in the market to help the companies mine the data and automate this whole process. These tools not only streamline the process but helps by providing suggestion in various steps. However, the information availability to the companies is huge and growing everyday in leaps and bounds. With the recent explosion of the information from both external channels such as the social media sites and internal channels such as BPOs, Collaboration mediums etc, it has become really challenging to mine such huge data to get the meaningful insight. But at the same time, ability of doing the same provides any insurance company a huge competitive advantage. 12
15 Key challenges with regards to typical Auto Insurance workflow can be: Verifying the data collected from the customer Profile customer behavior, and social networking influence Identifying the right insurance premium amount Verifying the claims raised Fraud detections by analyzing data from the disparate systems Exact claim reimbursement Solution and Architecture The Big Data approach brings in a new dimension in solution to the challenges mentioned above. With Big Data the approach in each stage mentioned above can be augmented by verifying the data collected from multiple internal and external sources and running artificial intelligence across all those information to identify any pattern of frauds or any other possible compromise. With Big Data technologies being in place, solution for two core challenges integral to these stages become practical and affordable 1. Ability to store and crunch any volume of data in a cost effective way 2. Ability to model statistically a rare event like fraud which needs sample data size close to the entire population to capture and predict right type of signatures of the rare events The potential data sources which can be leveraged for the same being Source: Internal Systems Collect the data about the customer from the Internal CRM system. Check the other products sold to the customer from In-house ERP, CRM systems. Check the credit history and based on that provide the necessary quotes from other internal systems. Source: External Systems Collect the data from the social networking sites regarding the behavior of the customer profile customers on their behavior, sentiments, social networth, usage patterns, click stream analysis for their likes and dislikes, behavior and spending patterns. Check the reviews and ratings of the product for which the insurance is required. For example if the insurance is required for the particular brand of car then reviews about the cars can be mined and by using sentiment analysis, the cars can be put in different categories. The cars with better reviews and ratings can be offered with lower premium and cars which have bad reviews and which brake down frequently can be offered with higher insurance premium. 13
16 Check the social network of the user. This is can help in identifying the status of the customer and even will help in identifying potential customers. Insurance companies can even make use of the data such as the customers interest in the car racing or his network which is quite active in rally racing etc. Some of the other data which can be obtained from the external systems includes the data to check the credit worthiness of the customer from the external 3rd party rating sites The solution architecture breaks the entire landscape into Acquisition, Integration and Information Delivery/Insights layers. Those three layers provide a seamless integration, and flexible options to plug-n-play additional sources and delivery channels hiding the underlying data complexity in each layer. The core to managing such architecture is towards handling the large volume of information, and the varied formats of data flowing into the system. Hence the solution s centered on big data and data virtualization techniques to manage this burst of information. Acquisition Layer Once the sources of both structured and un-structured data are identified, both externally and internally the right set of adapters and crawlers are required to extract the Big data from public/private locations outside the boundaries of organization. This is most critical aspect, and the right set of business rules on what needs to be pulled, requires an upfront preparation. Idea is to start with smaller number of sources which have more likelihood of having relevant set of data for desired outcome, and later extend this strategy to other sources. In parallel, is to leverage data integration and data virtualization technologies to integrate the relevant data sources which can provide auto insurance policy, customer details, product categories and characteristics, claims history. Tools like Informatica, IBM DataStage, ODI etc for data integration, and Denodo, Composite etc for data virtualization can be leveraged. Few parameters need to be factored in for better performance: Frequency of data capture from transactional systems, and external sources Delta feeds, trickle feeds and staging strategies can play an important role Effective business rules, technical rules filters in place to reject unwarranted data sets Integration This is where post extraction and filtering, the information gets consolidated under a common data model. Integration requires a strong set of mapping rules to map both structured and un-structured (transformed into structured format). The data model should support integrating following set of information: Auto insurance policy details by vehicle type Claim history by vehicle type and customer Customer feedback on product features sentiments, opinions 14
17 Premium Auto-Suggestion by Product Category Claims visibility by Product, by Customer Delivery Repair Warranty Identification Business Process Services Claim Submission Return Material Authorization Claims Adjudication & Credits Supplier Warranty & Recovery Holistic view of Claim Patterns and Customer Behavior on Product features Integrated Big Data Analytical Store Integration Product feedback by Customer Behavior, Sentiment Profiling Customer feedback on product Customer Behavior Patterns, Social Networking Product Services, Support feedback Product insurance and warranty claim patterns Acquisition Big Data Integration - Hadoop, GF5 etc Crawlers and Adaptors e.g. Scribe, Flume, Sqoop Un-Structured Content Facebook, twitter, Blog, Mail Data Integration, Virtualization Tools like Informatica, Composite,Denodo Structured Content CRM,ERP, SEA etc Structured Content Insurance, Warranty Applications Transactional Data, MasterData Data Files,XLS Customer behavior pattern analysis liking for red color vehicles, sports vehicles etc Customer social network analysis influence, and near neighbor financial capacity etc Delivery The final stage of delivering the insights generated from the integrated information as dashboards, scorecards, charts and reports with flexibility for business analysts to explore the details, and correlate the information sets for taking decisions on setting insurance premiums by vehicle and type of customers. The advanced analytics techniques in delivering such information will also help figuring out claim frauds, and unregulated auto insurance policies, claims. The delivery channels can be desktops via portals, mobile devices, internet based application portals etc. Business Value With Big Data technologies being in place solution for the two core challenges integral to the problem statement become practical and affordable 1. Firstly, the ability to store and crunch any volume of data in a cost effective way 2. Secondly, the ability to model statistically a rare event like fraud which needs sample data size close to the entire population to capture and predict right type of signatures of the rare events 15
18 And with that the immediate business values this solution can bring, can be categorized in Ability to tag right pricing on Insurance Premium with holistic view and better insight Better claims visibility and attribution to product insurance premiums/warranty Lesser fraud case and loss While this solution addresses the specific use case of auto insurance premium advisor, the natural extension of this solution and framework can certainly be applicable to Manufacturing parts warranty, other Insurance premium and claims, fraud detection processes covering domains like Manufacturing, General Insurance and Retail. Financial Services - Fraud Detection Use case context Fraud detection use cases are diverse and their complexity can be characterized by the fact that they are either real-time/batch, use unstructured data or only structured data, use a rules engine or derive a pattern (where the rule is not known but one is looking for any sequence of seemingly unrelated events which may be interpreted as a fraud). Here we focus on one such use case for elaboration and we will detail out the applicable solution and its architecture. Let s take an example of an online account opening form, which most of the financial service firms provide as an online service. The first step is to validate the identity of the user as entered in the form. Such forms can be misused by hackers to extract a valid combination of name, address, id, SSNID etc using brute force. Most of these applications would be sending a request to a web-service to check the validity of this prospective client and usually this web service is provided by an external vendor specializing in credit ratings. The requirement for the credit rating agency would be to intercept calls made to the web-service, look at the patterns of such calls and identify any fraudulent behavior. At the web-service end the characteristics of the challenges can be stated as follows The evaluation of individual request has to be done within seconds (depending on the SLA) Fraud evaluation would require historical data related to each request Evaluation would be based on a set of predefined rules- which may focus on all the request sent in the past predefined time window 16
19 Solution and Architecture Based on the definition of the problem and its characteristics, we would have the following technical requirements: Messages from Web Service Distributed Cache With counters and write ahead logs Server(node 1) RAM Distributed Columnar Data Store for Archived Data Disk 1 Message Bus Server(node 2) RAM Disk 2 Server(node n) RAM Disk n Query Processor - either A) picks the current value of the predefined Counter B) converts Query to map-reduce functions before processing 1. The System should be able to ingest requests at the rate they come in which will vary over a period of time 2. The latency of the system has to be below specified SLA limits which may not allow the system to store the incoming data before the response is to be evaluated 3. The need is to evaluate a set of incoming response which will need a good amount of RAM to accommodate the data in memory and any derived structures created to store the aggregated over history and current stream 4. With increased load the system may be required to use multiple nodes to ingest the data while keeping the overall validity of the counters across the nodes. a. The counters will be needed to be stored in a shared memory pool across the nodes. b. The counters will help reduce latency as they would be updated before the data is even written to the disk (for history updates) 5. Distributing the system over multiple nodes will provide the solution with parallelism and also ability to develop a fault-tolerant solution a. The distributed nodes as stated in the last point will be able to handle parallel writes across multiple nodes in a peer-to-peer architecture (not in a master slave architecture) 17
20 b. With increased number of nodes the probability of a node failure increases and hence replication of Historical data would be needed across multiple nodes. Similarly the data could be sharded across the nodes to help in parallelizing reads. Assuming, that the web service takes care of writing a message to the message queue for each request received with appropriate details, a Typical Architecture for such a solution will be composed of the following components 6. The Acquisition layer a. This component will read Messages from the message queue and distribute it to a set of worker processes which will continue with the rest of the acquisition process. b. Each worker process will be able to Look up the cache for an appropriate data structure based on the message details if found update the counter. Else create a new one. 7. The distributed cache - The role of the distributed cache will be to act as initial data store based on which the analysis could be done. Thus helping reduce latency between the message arrival and its impact on the measurement. This will need a. Initialization of the distributed cache while startup and also on a regular basis while data is flushed to the data disks b. Ability to Flush the data in cache to the data disk when the cache size reaches a certain water mark c. Ability to create local structure on the node where the message is received and replicate it to the copies situated on other nodes. d. Ability to create and maintain a predefined set of replicas of the data structure across the nodes to support fault tolerance 8. The Storage/retrieval layer a. Ability to store serialized data structures for the related processing nodes with adequate copies across multiple nodes to handle the fault tolerance in the data storage layer b. Ability to provide secondary index on the Data structures for alternate Queries The historical data stored will be time series in nature and columnar distributed data stores would be an appropriate way to handle this. c. The Data could be sharded across data nodes to increase the read response. Business Value Above mentioned solution provides opportunity to Reduce costs with ability to handle large volumes of varying load using commodity hardware 18
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Stealing My HeartbyGreenly©
So this is my first story ever. I would appreciate any feedback you could give. Keep in mind there has been no official editing. PLEASE let me know what you think if I should go on, or if you know of an editor I could use. Thanks!
She looked inconspicuous as she walked through the crowded streets with her hoodie pulled up over her long chocolate brown hair and her hands stuffed in her pockets. There was a festival occurring covering the streets with loud people, brilliant colors, and various street vendors selling their wares. Each vendor would push their merchandise in your face pretending to give you a good price on worthless goods.
But she wasn't there for the vendors she was discreetly watching the customers looking out for an easy mark. She was an excellent pickpocket. She could reach her hand in the inner jacket pocket of the most attentive cop and be half a mile away with their badge, wallet, and gun before they knew anything was missing. She didn't steal for a living but sometimes she needed the cash. Finding a job in this city proved to be more difficult than usual. Usually, a small hole in the wall diner or club could always use her waitressing skills. She didn't know where she was going to sleep tonight which was typical and also why she happened to be scouting potential mark. Sleeping in the park was getting dangerous. As the weather emerged so did the creeps and while she could take care of herself she didn't want to keep dozing with one eye open in this new city.
She was pretty much a gypsy moving from place to place on her own. She had no family and no roots holding her down so she moved constantly. She was convinced that one day she would find a place where she would instantly feel at home. It was a silly dream but it was the last one she held on to. She knew dreams didn't come true but when she closed her eyes at night finding that perfect place to call home seemed almost attainable. And something about this city seemed almost right.
So as she looked for her marks she made sure to choose ones that would provide enough cash to find a room to stay in while she continued her search for legitimate work. She examined the crowd through her dark lashes and finally found a good mark. She knew he would have a good amount of cash on him not only because of the fine, luxurious suit he wore with his serious expression but the men who were following him. They were watching and guarding him at a short distance. While most people wouldn't notice them she had developed a keen sense for watching and evaluating people. She briefly wondered why the man warranted a 3 man body guard team but she wiped it from her mind as she prepared to swipe his undoubtedly thick wallet.
She pulled down her hood and unzipped her sweatshirt to perfect her façade. Blending in was the key to her pick pocketing. She needed to be causal but still with a bit of apprehension that all individuals had in a large crowd. The more blended she could be the less likely his guards would perceive her as a threat and the closer she could get to the mark. She began clutching her bag and looking around cautiously. Assuming her role she eased up to the well dressed man and brushed up against him while looking in her bag. As she reached into his bulging pocket to retrieve his wallet she felt his skin brush her hand and she paused. A tingle caressed her hand and shocked her. With her hand in his pocket she lifted her eyes to meet his. Her brown eyes met brilliant green orbs set in the most beautiful face she had ever seen. His soft tanned skin seemed to melt over the strong masculine lines of his jaw and cheekbones. His full lips parted in a small smile. His glossy black hair fell to his shoulders while framing his face beautifully. In that brief moment his eyes seemed to bore into her soul and she had a dazzling vision of the sun shining through the leaves of the forest and kissing the forest floor. In her peripheral vision she noticed his hand coming up to caress her face and it brought her back to reality. She snatched the wallet out of his pocket, glanced around to locate and evade the green-eyed man's bodyguards and dashed off. She had her prize but the striking smile on the man's face was etched into her mind.
Xavier Cambridge was stunned. This was a rare occurrence for the Alpha. He felt the tiny human bump into him. Her head barely reached his shoulder but in their brief moment of contact her scent floated up to his nose and interrogated his senses. She smelled like warm honey and roses. She smelled of sunshine and happiness. It warmed the empty, lonely depths of his soul and called to the animal within him that had lay dormant and uninterested for so long. He hadn't felt his wolf stir so strongly in such a long time. And when he felt the caress of her skin and saw the sparkle in her shocked hazel eyes he had to know everything about her. He was not prepared for her hasty retreat and was too distracted by the silky bounce of her thick brown hair to stop her.
Xavier's Betas noticed the faint smile and the girl's retreat and rushed to his side. They hadn't seen a true smile on his face in years. Xavier didn't know what to say or where he was headed anymore. Content to just stop and stare while he figured out a way to find her again his Betas urged him to keep moving.
As Alpha he had many duties. Currently he was on route to a meeting with the Alphas of the three closest neighboring packs. Alphas Terrence, Carson, and Michaels were waiting to meet with him regarding territory lines. While the city proper was neutral territory the outlying country was clearly divided. With the increasing numbers in the Cambridge Pack, Xavier was trying to prevent any complications from arising. Tension had already been high with the Terrence Pack due to deep rooted rivalry that went back centuries.
When Xavier left his compound to journey into the city that morning he wanted to confront the concerns of the other packs and return home as soon as possible. He hated the congested streets of festival days. Now all he could think of was searching every face in that insufferable crowd for the bright hazel eyes and soft pink lips. The girl with the caramel skin and honeyed scent swam through his mind. Every girl he saw would suddenly take on her features until he turned to look directly at them. His wolf growled in frustration as they made their way through the streets. Derek, Xavier's second in command and close friend began to chuckle.
"What amuses you Derek?" Xavier asked
"You do Alpha. We all saw that exchange between you and that girl. Now you flinch every time you see a speck of brown hair" Derek, Tomas, and Zach's chuckles became roaring laughter causing strangers to look in their direction and step away from the four giant men.
"We'll be late if you don't stop that Alpha" Zach added always the most practical.
"Girls have been throwing themselves at you for ages and I've never seen you make that face." Derek countered, trying and failing to stop his laughter.
"What face?" Xavier asked
"That mushy lovey dovey face" Tomas exclaimed grabbing his stomach and wiping tears from his eyes.
"Enough" Xavier growled not amused anymore.
"Let's be on our way."
Xavier's wolf howled within him. He wanted the honey and rose scented girl. He wanted to find her, mate her, and hold her close for all of time. His wolf projected these images to him causing his skin to heat up and blood to boil within him. Find her! His wolf cried. Bring her home to us! Xavier's wolf warred with him. Not yet. He told his wolf. We must make sure our territory is safe and meet with the Alphas. Then we will find her and never let go! He begged his wolf to understand he had to go to this meeting.
Xavier craved this new found girl with a fierce longing he'd never know before. A few mere seconds of contact and his life was changed. His wolf grew weary as the trudged on a waited for the moment when they would shift and track down their hazel eyed girl.
"Were late Alpha" Zach repeated. "I'll track down the girl later. . ."
"You will not touch her!" Xavier growled fiercely turning to bear down on his Betas who backed up slowly. "She's mine. All mine. My mate" The words came out before he even realized what he was saying.
"My mate?" he asked himself quietly as he spun around and continued on his way. His wolf hummed contentedly at his proclamation. Xavier found even though he didn't know the girl's name yet thinking of her as his mate was exceedingly pleasing. His Betas laughter quieted down as they realized the magnitude of the situation. Their Alpha had just found and lost his mate. They were happy for him and immediately started scanning the crowd for the girl that had claimed their Alpha's heart. Their new Alpha Queen had to be found and secure.
As the group drew nearer to the meeting place Xavier noticed an amber jewel glinting in the sunlight. The jewel was hanging on a black woven necklace. The effect of the jewel surrounded in black reminded Xavier of his mate's eyes as she peered up at him through her thick dark lashes. He had to buy it for her. The first gift of many he would present to his lovely mate. It was then, as he reached into his pocket, that Xavier became aware of the absence of his wallet. In that moment it all clicked.
"She stole it!" he exclaimed. He should be furious, angry that someone got close enough to take advantage of him. He was an Alpha it shouldn't have happened.
"What?" Derek asked confused.
"She stole my wallet" and laughed a loud and deep belly laugh. He should be livid, enraged. But he wasn't. He was impressed and eager to learn more about his mate, the beautiful thief.
His Betas thought he was cracking, losing his mind as he laughed uncontrollable for the first time in a long time. They exchanged entertained glances and shook their heads.
"I'll get through this ridiculous meeting, then I will find my little pick pocketing mate and she will be mine forever" Xavier thought as he grinned and walked past the booth selling the amber jewel and into his meeting.
The small group didn't notice the hazel eyed girl perched on a low rooftop watching their leader. She was too far away to hear what they were saying but she couldn't tear her eyes away from the exquisitely alluring man. He entered the building and she turned away prepared to never see him again. But somehow that saddened her.
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kapteeninkulta, phryn3 and 421 other people favorited this story!
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- by Anonymous12/30/17
Good story....... unfinished is the true tragedy
This is a delightful tale and have read it multiple times...... the true tragedy is that it is UNFINISHED. The author did a most exquisite job.by ChasingtheSky10/21/17
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text:
This section contains 660 words
(approx. 2 pages at 400 words per page)
Claymore took Burly's truck. He hoped the cops wouldn't think he killed Burly. He recalled Alabaster's question: "What happens if someone finds a way to stop death?" (213) He called the phone number that Alabaster had given him. When Alabaster answered Claymore told him that his sister was trying to kill him. Alabaster told him to come immediately to the address on the sheet of paper.
Alabaster's "father" answered the door. A smile was frozen on his face. The house was meticulous. Everything looked like it just came out of plastic wrap. Alabaster came in the room and told Claymore to ignore his companion who was not his father but rather a Mistform that Alabaster created to pose as his parent so no one would ask questions. To prove his point to Claymore, Alabaster made the Mistform...
(read more from the Son of Magic (Pages 212 - 227) Summary)
This section contains 660 words
(approx. 2 pages at 400 words per page)


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So, all of us who have amd64 machines have suffered heavily in the past few years. That goes for all 64 bit systems, but we were the first. :) The big problem is that everybody and their dog only release drivers for 32 bit systems, at least the cheap junk we always buy.
So when I did a 'full upgrade' to my big machine, I found I couldn't print to my laser printer. It only had an old 32 bit driver and no chance for a 64 bit one. That was a total nightmare and I solved it by a total new install. Thus, my older machine waited forever for an upgrade, and I finally had to do it.
So, when you do a full upgrade, the printer driver (filter program) won't work. This is Debian Multiarch. You used to be able to just load the 32 bit libraries, but you can't do that any more.
In root, type dpkg -i --add-architecture i386
NOT ENOUGH! as I found out.
Immediately do an aptitude update. Then when you reinstall your driver, it will tell you what 386 libraries are missing, and you can actually load them. Install the driver again, and it works.


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Imperial Towers (Book of Never #5) is now available at Amazon and Kobo and very soon at B&N and (hopefully) at Google Play also.
Purchase - Amazon & Kobo, All Territories
The link above is a 'global link' so no matter where you are in the world, the link will send you a page where you can choose between Amazon, B&N and Kobo, which then defaults to your territory :)
Big thanks to Never fans who have followed along since March 1st in 2016, exactly a year ago today, when book 1 (The Amber Isle) was released :)
Stay tuned for an update for B&N customers but also several interviews where I talk about Never, the books and the magic, along with a post of me reading chapter 1 of Imperial Towers :)
Free ebook with every newsletter sign up in 2016
Ashley Capes is an Australian writer of fiction, poetry and very occasional non-fiction.
Progress Meter
Imperial Towers (Never Book 5) - draft 1
Moss Dragon - draft 1
Reed Lavender (working title) - draft 1
Unnamed Spec Fic - draft 1
Whisper of Leaves (sequel) - Outline
August 2018


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The Juno Trac 12 LED Mini-Flood offers effective flood and task lighting in a low-profile, energy-efficient package. It approximates the lumen output and light distribution of a 20W halogen flood, while utilizing less than 25% of the energy and providing a rated life of 50,000 hours. Available in 3,000K and 4,100K color temperatures.
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We value our patients' experience at Gateway Family Chiropractic. If you are currently a chiropractic patient, please feel free to complete a review on one of our social media pages that are linked in the top right corner of the page.
Dr Mike Hughes | Dr Dave Hughes | Dr. Shannon Bundy
Your Loveland Chiropractors
Baby Merrick - Listen to Baby Merrick's mom excitement after visiting our clinic.
-Cara M., Wellington
"After just a few visits I felt relieved and confident that Dr. Dave and Dr. Mike had a very similar touch as Dr. Dahl. They have already given me great relief with some of my tricky adjustments! Thank you!"
-Galina G., Fort Collins
"I have a 20 year old son who has Hemihypertrophy, which is a condition in which one side of the body is slightly larger than the other. He has had multiple surgeries and shoe lifts throughout his life and on the first visit to Gateway Chiropractic, the Hughes Brothers were able to align his spine and create instant relief and better posture. It was amazing. As well as I have had great relief myself from Dr. Mike, he is the only one I would trust or refer to anyone."
-Kristin C., Longmont
"I started treatment with Gateway Family Chiropractic in December 2013 for CRPS (chronic regional pain syndrome). I have been down medical route for seven years bouncing around from specialist to specialist with very little improvement at all. My body was poisoned with 21 different drugs and hard narcotics which lead to fibromyalgia and other complications. After a thorough exam and starting care with Dr Hughes they were able to find so many things about my health and body that no doctor was ever able to find. Dr Michael & David Hughes put me on some supplements that help me lose 20 pounds within the first two weeks because of a missing gallbladder. After a few treatments, I have never felt so good! My pain has dramatically reduced from 10/10 to 2/10 pain scale. Now I come in one time a month for maintenance and have no complaints.
-Bruce E. Firestone
"Most friendliest of staff, always so welcoming and very understanding of all situations! I always look forward to going into my appointments and getting adjusted! It's unlike any adjustment I've ever seen before, they are very thorough and don't touch you unless they know what an Xray and Nerve scan look like so it's very comforting to know that they won't go changing things that don't need changing just to earn a penny! Highly recommend them to anyone and everyone! The only reason I went to them in the first place was because of their reviews, and I don't regret the decision one bit!!! It's the best decision I've ever made!"
-Natasha Y., Loveland
See more success stories on our social media channels.


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The Sprout Foundation
Planting seeds for our future by supporting early childhood endeavors and, separately, preserving our environment.
Since 2005, the Sprout Foundation has supported non-profit organizations that share our passion for preserving the environment and promoting the healthy development and well-being of children. The Sprout Foundation is a 501(c)(3) private family foundation that serves the Denver Metro Area and Grand County. We award grants quarterly, on an invitation-only basis, for both capital and operational needs.
Our Mission
The Sprout Foundation continues to support our two original concerns—children and the environment—but our mission has been refined to focus on early childhood programs or preserving the environment. (If you are in Grand County, please click on the Grand County link, as our support is slightly broader.)
We have honed our mission to maximize our impact, better communicate our goals to possible grantees, and ensure our team spends as much time as possible on mission-focused grantee opportunities. With this in mind, the Sprout Foundation will consider funding 501(c)(3) organizations serving this geography and offering:
- Early childhood programs that support academic readiness and/or promote healthy development and optimum mental health, and that demonstrate measurable outcomes
- Hands-on environmental programs or projects that impact our local environment directly (or more remotely, such as those that impact our water supply or the air we breathe)
Learn more about our funding priorities or see a sample of representative grants. Please note: the Sprout Foundation is not affiliated with the grocery chain Sprouts Farmers Markets or related foundations.


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Ghostly phenomenon baffles town: 'I wish I knew what it was'
In 1966, a group of high school boys were out at Michigan's Ottawa National Forest when they witnessed something so frightening they ran straight to the local sheriff. The boys witnessed what has since been dubbed the Paulding Light, which witnesses describe as a bright white light glowing amid the trees, deep in the woods.
…Read more


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Looking forward to the 2006 season I thought that I would put out my own take on a possible (but in truth not all too likely) 25 man roster. I will try and comment on the players that would be new to the team or controversial.
Starting lineup
C) Micheal Barrett (.276/345/479): Some want to trade him due to some bad defense. To that I say "LOOK AT THAT BATTING LINE! LOOK AT THE BRAND NEW SILVER SLUGGER! THE MAN CAN FLAT OUT RAKE!"
1b) Derrek Lee (.335/.418/.662): Nothing wrong there.
2b) Todd Walker (.305/.355/474): The arguments for/against him are basically the same as Michael Barrett.
3b) Aramis Ramirez (.302/.358/.568): Top 5 thirdbasemen in baseball.
SS) Ronny Cedeno (.300/.356/.375): He could tank and be a flaming pile but he has earned the chance to become that flaming pile.
LF) Matt Murton (.321/.381/.521): Is he going to put up those numbers over the corse of an entire year? Doubtfull. But I think a .290/350/450 line is well withing reach.
RF) Pat Burrell (.281/.389/.504): Alright his is where the team starts to stay from last year. The Cubs desperatly need to get some more offense out of the corner outfield and I don't want to put too much on the shoulders of Murton. I think that a trade of Kerry Wood plus Rich Hill should do the trick. The money even comes close to evening out.
CF) J.D. Drew (.286/.412/.520): I think this is possible. The Dodgers are going to try and trade away most of Depodesta's "Moneyball" moves. I hope the Cubs can capitalize on that and pry away a great player. Drew rates as a very good defensive centerfielder by BP's metrics and Wrigley has a small centerfield. I am not sure what the Dodgers would want for Drew. This may have to be a large minor league player say Pie or Harvey and several pitchers.
The Bench
C) Henry Blanco (.242/.287/.391)
IF) Mike Fontenot (.272/.377/.430) AAA Iowa: I think he is ready for the majors. Cheap backup for A-Ram and Walker.
Calos Pena (.235/.325/.477): This is the pop off the bench that the Cubs have been missing the last few years. He is also a lefty so thats good. I may try and have him try a corner outfield spot just in case of emergency.
Kazuo Matsui (.255/.300/.352): This move is one big if. If the Mets try and get Furcal (it is in the papers today so thats all I need) then they will want to get rid of Matui. So If they Mets agree on picking up most of his contract I think he would be a great pinch runner/defensive replacement. I also have a suspition that his numbers will go back to 2004 levels.
OF) Jason Michaels (.304/.397/.415): I LOVE Jason Michaels. He is a very strong defender and would be more then capable if Drew goes down for a few months. I wouldn't even mind giving Michaels the starting job if Drew or Bradley don't work out.
Brandon Sing (.276/.404/.538) AA West Tenn.: Just one more good bench hitter that the Cubs have been lacking. Is Sing ready for the Majors? I think so, but I could be wrong. That is a risk I am willing to take with the last man on the bench.
The Rotation
Carlos Zambrano (223 IP, 3.26 ERA)
Mark Prior (166 IP, 3.67 ERA)
Greg Maddux (225 IP, 4.24 ERA)
Jerome Williams (106 IP, 3.91 ERA)
Glendon Rush (145 IP 4.52 ERA): We gave him three mill per, that is too much not to be in the rotation.
The Pen
Ryan Dempster (92 IP 3.13 ERA) Throws (R): God I hope that arm holds up.
B.J. Ryan (70 IP 2.43 ERA) Throws (L): Relief studd. He would end up being a lot better than our closer.
Will Ohman (43 IP 2.91 ERA) Throws (L): Solid all around LOOGY maybe better.
Michael Wuertz (75.7 IP 3.81 ERA) Throws (R): He was one of the few highlights of last years pen.
Jermaine VanBuren (54.7 IP 1.98 ERA) AAA Iowa Throws (R): Give the guy a shot already.
Novoa/Willliamson/Wellenmeyer: End of the pen guys. Williamson could easily become the best of the lot but I am not holding my breath. I could also have thrown Guzman, Koronka, Mitre, Nolasoco, ect. into the mix. The Cubs have these types of arms in droves lets hope a few work out.
Pros: This linuep can flat out mash. The first eight guys in the starting linupe had OPS' over .800 last year. Runs would be scored. The bench provides lots of versatility and is no longer the black hole it has been. The rotation is solid. The pen could be very scary for opponents.
Cons: Not much speed. In fact our first basemen might be the best basestealer in the starting lineup. Not one soul in the lineup is a "bona-fide leadoff hitter" and the cubs could end up with Todd Walker hitting leadoff all year. (I don't care about this last complaint but some do) There is not much in the way of left handed hitting. There is not much help if a member of the rotation gets hurt. Our top two prospects (Pie and Hill) would be gone. This is not to mention that three of my trades (Drew, Burrell, Matsui) have big ifs attach. The pen could be very scary for Cub fans.
Conclution: I obviouly like this roster but of course I do I made it. Is it a bunch of wishcasting? Yes. I traffic in fun speculation.
I haven't mentioned what happened to some players on the Cubs last year Hairston, Patterson, ect. and I would guess that they would just be included in some of the trades. Specifics can be a bitch. I welcome any comments.


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Marco's New York Italian by Marco Pierre White, Birmingham Airport
Coventry Road, Birmingham, B26 3QW
- Average
- Main course: £12
- Food spend: £22
- Opening hours
- Monday - Sunday - 12:00 - 21:30
Great service and great food
20 December 2016
from Birmingham
Taking its name from world-famous chef Marco Pierre White, Marco's New York Italian at Birmingham Airport blends American and Italian dining, served in an ultra-modern, trattoria-style setting. The plush, monochrome dining room features black and white prints of the man himself.
Along with homemade pizzas and pasta, juicy burgers and steaks, the Marco's New York Italian menu offers more elaborate creations. Try the yellowfin tuna steak with spinach, puttanesca and new potatoes, or the lamb rump with Italian beans and rosemary and garlic roast potatoes.
A side of 'lazy fries' comes highly recommended, no matter what you've ordered. Indulge in a melting chocolate truffle cake for pudding at Marco's in Birmingham, or opt for a zingy Italian delight of ice cream swimming in limoncello.
Found at the Holiday Inn, Marco's restaurant is conveniently located 10 minutes from Birmingham International Train Station, Birmingham International Airport and the NEC / Genting Arena.
Marco's New York Italian by Marco Pierre White, Birmingham Airport
Coventry Road, Birmingham, B26 3QW
See all Marco Pierre White Restaurants
See all restaurants in Birmingham
Customer reviews for Marco's New York Italian by Marco Pierre White, Birmingham Airport
- Food
- Value
- Ambience
- Location
- Service
- Drinks
((((( 20 December 2016 by Tony from Birmingham
Great service and great food
((*** 30 November 2016 by Christine from Birmingham
The sharing platter was delicious and great value. However, as the restaurant was only open for voucher users there was no dessert menu. The staff seemed a little confused by the whole concept of the 'voucher'. We were not encouraged to stay once our meal was over - no coffee offered.
(((** 8 November 2016 by Marianne from Worcester
Good quality food. Puddings a bit measly and drinks very over priced.
((*** 28 September 2016 by Nichola from Birmingham
Had a pizza and prosecco Wowcher offer. Wasn't clear that there was only two pizzas to choose from. Booked on a Sunday afternoon which we expected to busy. On arrival there were only two other tables being used. So therefore lacked atmosphere. Pizza took 45/50 mins minutes however the staff did apologise for delay
((((* 28 September 2016 by Alissia from Birmingham
Was annoyed at the start faffing about for the voucher I gave plenty of notice for them to let me know in advance if they needed a copy I noticed when I got home later that nighg the mgr had emailed me and 1.5 hours before my reservation for it...too late then was getting ready and travelling to restaurant. Also told I had no reservation on arrival when I know I did and spoke to a Jess to amend the time on Saturday.
Anyway after all that was out the way things slowly got better thanks to our waiter Ashish he was amazing and I can see why he is a team leader! Thank you Ashish.
The mushroom starter was tasty but way too small compared to the wings I saw come out it was not value for money. The pizza that I had was very big and tasted good. My mum had sea bream as no sea bass left again a very small piece of fish and hardly any vegetables as described but tasted fine. Best thing was dessert where Ashish gave me a lovely surprise and brought me out 2 as I couldn't decide this was a real delight and for this gesture alone would make me return as that is service recovery at its best. The cheesecake was really lovely the brownie was ok.
The portion sizes are inconsistent some things are very tiny some are very large some are just right; that should be looked into. Also they should make you aware of any items not available as soon as you are seated but Ashish did his best to offer alternatives. Nice atmosphere and free parking.
Overall had good and bad points would give it 1 more go on the a la carte menu as it has potential.
((((* 27 September 2016 by Keith from Birmingham
slight mix up with order but soon resolved
((((* 26 September 2016 by Paul from Dudley
Excellent food and a great deal
(((** 23 September 2016 by Angela from Birmingham
A bit noisy as building work was going on at the time. Service good, food acceptable.
((*** 14 September 2016 by Shereena from Birmingham
Nice decor and booking was easy. The starter and main courses were quite nice but the Service was very poor though as had to wait at least 20 minutes for our drinks and 30 minutes for our starter. Shame as it appears to be a nice atmosphere but the food and service let it down as the desserts - New York Cheesecake tasted like rubber and Italian Doughnuts were very greasy.
((((( 13 September 2016 by Julie from Kegworth
Everything was really good, the service was excellent, the staff were very friendly and helpful.
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