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Big Data Building Blocks with AWS Cloud - Ebook


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This Ebook gives an idea about how Big Data has become an indispensable part of any organization, and how exactly it can be tackled using the Various AWS Cloud Components.

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Big Data Building Blocks with AWS Cloud - Ebook

  1. 1. BIG DATA Building Blocks with AWS CLOUD
  2. 2. HOW TO CLOUD IT BETTER WITH BLAZECLAN A 360 Degree Cloud Solution Big Data Solution Data Warehouse on AWS AWS Boutique Consulting Managed Service On AWS Application Development AWS Infrastructure Managed with Our 360 Degree Approach Delivering Scalable, Reliable & Cost Effective Applications on AWS Cloud Unleash the Potential of Your Data with AWS Cloud AWS Consulting Tailor Made for your Business Needs Making Amazon Redshift work as your Data Warehouse Solution Request a 2HR Consultation FREE
  3. 3. KINESIS REDSHIFT EMR DYNAMO DB Written by Varoon Rajani | Designed & Compiled by Neil Varoon D. Rajani Founder & President Cloud Consulting With more than 10 Years of Experience in Designing and Developing Enterprise Database Solutions Varoon is an AWS Certified Solution Architect & Product Manager for Cloudlytics, a Big Data SaaS Solution built using AWS Components. His Passion lies in Cloud Computing with an Entrepreneurial Zest for Adventure! A Digital Marketing Strategist born in the Clouds. Neil Manages the 4 Essential Elements of the Digital Space – Content, Analytics, Social & Leads for BlazeClan. In his spare time he questions philosophies & Strums his Guitar! Nilotpal (Neil) Roy Digital Marketing Strategist
  4. 4. KINESIS REDSHIFT EMR DYNAMO DB CONTENTS Why All the Buzz & the Fuzz about Big Data? Why is Cloud Big Data’s Best Friend? Four Major Challenges of Big Data you can’t Ignore The Big Data Life Cycle How AWS Components Fit in the Big Data Life Cycle (Transfer, Storage & Databases) Hadoop in the Clouds using AWS’s EMR What are the Big Opportunities with Big Data? Some Interesting Big Data Facts 1. 2. 3. 4. 5. 6. 7. 8. 9. Big Data use cases across various Verticals
  5. 5. CHAPTER 1 Why All the Buzz & the Fuzz about Big Data?
  6. 6. KINESIS REDSHIFT EMR DYNAMO DB A Jump Start Guide to Big Data on AWS Cloudl6 Well, as you finish reading this Sentence, more than 49k Tweets will have been chirped, 168000k Emails Bombarded, 350k Facebook Statuses Fired, 600+ YouTube Videos uploaded and so on, in the virtual space. Check the Big Data Fact Sheet for more of these Facts! Big Data simply put is data, which cannot be processed by the current tools & technologies that we possess. So is Big Data something new? Well No, Big Data existed since long back, where companies like Google had the resources to process & Analyze it. But with the Evolution of Cloud computing, Open Source Frameworks & lowering of Hardware Costs these resources to process Big Data are now available to independent small businesses, startups & even individuals. So what if I don’t have to tackle millions of DNA strands or monitor the fluctuations in the Stock market, do I still need Big Data? Even If I have a small business? Well the answer isn’t a straight forward yes, but imagine…… Is the Big Data Story Really that Big…
  7. 7. KINESIS REDSHIFT EMR DYNAMO DB If your newly launched product feature created a buzz on social, with thousands of Statuses & Tweets being Fired across the Globe (yes it does happen), won’t it be amazing if you could track all of those in a sentiment analysis & gather more insight into your customers? In today’s ever changing world the only way to stay ahead of the pack is to predict & react to the change even before it happens. For your business to do this, a continuous feedback loop of human & machine generated data needs to be processed. Well that, in effect is nothing but cracking Big Data! After web 2.0 & the explosion of the internet almost all the data generated in these feedback loops are from the virtual world, like sampling surveys, feedback forms, device/mobile sensors and many more. With these mountains of data piling up, the irritating feeling at the back of your head can be summed up in three words. Volume, Velocity & Variety. These are the 3V’s of the Big Data challenge. Let’s look at these 3Vs a bit more in detail. A Jump Start Guide to Big Data on AWS Cloudl7 What to Expect?
  8. 8. KINESIS REDSHIFT EMR DYNAMO DB The 3 Vs - VOLUME Everyday a total of 2.5 quintillion bytes (Exabyte) are flooding into the virtual world! And by all probability by the time you finish reading this Ebook the number would have already exceeded by a few Exabytes! Imagine the Growth since year 2000 when only 800,000 PB(Petabytes) of Data was Stored Globally. Having trouble switching between all the Gigas, Petas & Exas? Well there is a simple conversion; we all know that 1kb is roughly1000 bytes. So all the units of measurement that follow are Mega<Giga< Tera<Peta<Exa< Zetta< Yotta<Bronto<Geo with the suffix byte attached to them, where each unit is roughly 1000 times the previous unit. So just to put things into perspective, every two days we create as much Information as we did from the Dawn of civilization until 2003! This volume cannot be handled by the traditional IT systems with data processed in batches; it could take weeks. But today data can be processed in massively parallel architectures, the MapReduce framework that was introduced by Google, has played a major role in evolving the parallel architecture used in Big Data Analytics. A Jump Start Guide to Big Data on AWS Cloudl8
  9. 9. KINESIS REDSHIFT EMR DYNAMO DB Big Data is generated from social networks, various sensors installed at store entrances, traffic lights, in airplanes, Car GPS and countless other sources. All of this data is in varied formats, sometimes not digestible by the existing systems in their current form. These variations in the formats make it differ from the way current systems store the data, which is stored in a well-defined schema in a Relational Database. All the data fits in nicely and is easy to understand and analyze. These systems with static schemas cannot handle variety. The success of the organization depends on analyzing the data in variety of formats and make business sense out of it. Big Data processing helps organizations to take this unstructured data and extract meaningful information, which can be processed by humans or structured databases. The 3 Vs - VARIETY A Jump Start Guide to Big Data on AWS Cloudl9
  10. 10. KINESIS REDSHIFT EMR DYNAMO DB The Velocity with which this data is generated is phenomenal & makes it virtually impossible for the conventional systems to handle it. Twitter & Facebook generate around 5 GBs and 7 Giga Bytes of data per minute respectively. There are numerous other organizations which generate data at equally faster rates. The current technologies may allow storing this streaming data, but the challenge lies in analyzing this data while still in flow and to make business sense out of it. Organizations looking for competitive advantage over each other therefore want this analysis done in seconds or even micro seconds, faster than their competitors. For example, consider the case of financial markets where a fraction of a second can help organizations make enormous profits. This Need for Speed has led to development of various Big Data streaming technologies and fast retrieval technologies like the key-value stores and columnar databases for static data. The 3 Vs - VELOCITY A Jump Start Guide to Big Data on AWS Cloudl10
  11. 11. KINESIS REDSHIFT EMR DYNAMO DB The New Vs- VERACITY Big Data Veracity here would refer to the biases, noise and abnormality in data. This might raise a question of whether the data that is being stored, and mined meaningful to the problem being analyzed. The answer to it would be that veracity in data analysis is the biggest challenge when compares to things like volume and velocity. In scoping out your big data strategy you need to have your team and partners work to help keep your data clean and processes to keep ‘dirty data’ from accumulating in your systems. A Jump Start Guide to Big Data on AWS Cloudl11
  12. 12. KINESIS REDSHIFT EMR DYNAMO DB The New Vs- VALIDITY & VOLATILITY Like big data veracity is the issue of validity meaning is the data correct and accurate for the intended use. Clearly valid data is the key to making the right decisions. The Big data volatility refers to how long is data valid and how long should it be stored. In this world of real time data you need to determine at what point is data no longer relevant to the current analysis. Big data clearly deals with issues beyond volume, variety and velocity to other concerns like veracity, validity and volatility. A Jump Start Guide to Big Data on AWS Cloudl12
  13. 13. CHAPTER 2 Some Interesting Big Data Facts
  14. 14. CHAPTER 3 Why is Cloud Big Data’s Best Friend?
  15. 15. KINESIS REDSHIFT EMR DYNAMO DB Deploying Hadoop Clusters is Not Easy! Before we look into this question we need to understand the criticality of a Big Data requirement in the various industry verticals. A research survey by Gartner in 2011 showed how there is dramatic rise in data generation as compared to data available for analysis, which means that more and more data is flowing out unchecked. Gone are those days of sampling from a data set to predict trends. The new age is a Data Driven one, where the schemas to tackle business processes are now being built on Data. With the 3Vs of Data kicking in, larger data sets have to be monitored & analyzed through different parameters for results that can truly drive business decisions. To tackle data in larger & faster flux through a wide variety of mediums organizations need a ton of tools, resources, finances & time. Even to Set up a Big Data infrastructure may take weeks. You will need a mix of both Infrastructure/hardware scale as well as the software/platforms/ecosystems set up, and then maintained over a period of Time. A Jump Start Guide to Big Data on AWS Cloudl17
  16. 16. KINESIS REDSHIFT EMR DYNAMO DB So here, we come across a few challenges some of which you might have already identified:  Infrastructure Challenge in terms of Storage & Compute Elements.  Software Challenge in terms of choosing, configuring & maintaining Platforms & ecosystems  Man Power challenge of pooling talented data scientists for setting up &  maintaining  Time challenge of setting up a Big Data infrastructure & then managing it.  Tons of maintenance issues starting from cooling machineries to patching up codes & updates.  Inelastic capacities, meaning you lose out on real time data during peak times of business & pay for capacity you are not using for the rest of the year. What are the Challenges? A Jump Start Guide to Big Data on AWS Cloudl18
  17. 17. KINESIS REDSHIFT EMR DYNAMO DB The Figure above shows how Cloud-Based infrastructure gives your Data wizards the majority of their time in actually putting their Big Data findings to use and innovate. So where does Cloud come into the picture? Well, all the above issues can seamlessly be solved when you’re on the Cloud. We define Cloud computing through its various benefits, like: • Elasticity • Pay per use • Unlimited Scale • Managed Services How to Overcome these Challenges? A Jump Start Guide to Big Data on AWS Cloudl19 If you think about it, these help us in just the right way to deal with our Big Data conundrums! Now you can Collect, Store, Analyze & Share (The Big Data lifecycle which we will talk about in a later chapter) your Big Data findings. Also you pay for just the resources you use for the time that you use it for.
  18. 18. KINESIS REDSHIFT EMR DYNAMO DB A recent Study by IDC showed that “Over the next decade, the number of files or containers that encapsulate the information in the digital universe will grow by 75x.” While the pool of IT Staff available to manage them will grow only slightly at 1.5x”. This fact materializes our growing fear. If all our Data Scientists are busy setting up & managing Big Data systems, then it leaves very less time to actually work with the findings and drive innovation. Software / Hardware Conundrums? A Jump Start Guide to Big Data on AWS Cloudl20 What about the Software/ Hardware Conundrums? We need flexible language choices with easy programming models which are designed for distribution. Also you would need platforms for abstraction & an apt ecosystem. Hadoop is the great example for the software aspect whereas Cloud Computing is just the right fit for your hardware needs. So wouldn’t it be great if you had something like Hadoop in the Clouds? Well we have something just like that in AWS Cloud which we will discuss in the later chapters.
  19. 19. CHAPTER 4 Four Major Challenges of Big Data You Can’t Ignore
  20. 20. KINESIS REDSHIFT EMR DYNAMO DB Business Decision makers everywhere yearn for the right information that would help them make informed decisions. 30 years back business heads had a challenge of collecting enough data to make informed decisions. Today, the tables have turned, where they have so much data that it is impossible to make sense out of it! Big Data is helping organizations of all sizes to make better business decisions, save costs, improve customer service, deliver better user experience, and identify security risks among other things. We read about Big Data everywhere; we listen to it from experts everywhere; even governments are talking about it. So what are these 4 challenges that we need to consider? • Ownership • Data • People • Technology Challenges of Big Data A Jump Start Guide to Big Data on AWS Cloudl22
  21. 21. KINESIS REDSHIFT EMR DYNAMO DB Who Owns the Data? Big Data is not a technology initiative, but a business one. The Big Data initiative has to be driven by the leaders of the organization, be it Business Heads or CXOs. It can help an organization to improve operational predictability, increase sales and improve customer service among other things. These outcomes of the initiative have to be identified and articulated by the Business heads. Additionally, the procedural and in certain cases structural changes brought in by Big Data have to be managed carefully. Organizations do not change easily and not everyone may appreciate the value brought to the table with advanced analytics. This is a typical organizational challenge that needs to be handled aptly by the top management. Organizations have to be sure not to label Big Data just as an IT driven initiative. A Jump Start Guide to Big Data on AWS Cloudl23
  22. 22. KINESIS REDSHIFT EMR DYNAMO DB How Important is the DATA?? The most important aspect for any organization to benefit from Big Data is the data itself. While there is variety of data collected by various tools and processes, not all data is relevant. It is critical for an organization to identify relevant sources of information depending on the outcome expected out of the effort. For example, say if you want to improve customer experience on the website, an example of relevant data would be log details about the errors encountered by users while connecting to your website. In this case, you may not want to store or process the log details of successful connections. Only when there is relevant data, it can be processed and organized in a way that provides meaningful insights to the management to make informed decisions. A Jump Start Guide to Big Data on AWS Cloudl24
  23. 23. KINESIS REDSHIFT EMR DYNAMO DB Who Call the Shots? For any successful Big Data effort there has to be a team of people with the right skills. As I pointed out earlier, Big Data is not a technology initiative, and the skill sets required are not limited to technology. For a successful Big Data effort, the team should have right mix of, Data Scientists: Data scientists with their skills and expertise help in deriving right statistics and identifying patterns to correlate variety of data and bring out meaningful insights. Technology Experts: Technology Experts who bring in specific skill sets to drive the technology that forms the backbone of the Big Data initiative. The team of technology experts will be able to identify the right set of software tools and hardware infrastructure required. Business Owners: Business owners who can drive the Big Data effort by defining the outcome of the Big Data effort and then working with the technology and data scientist teams to achieve the outcome. A Jump Start Guide to Big Data on AWS Cloudl25
  24. 24. KINESIS REDSHIFT EMR DYNAMO DB What Technology is at Stake? Technology forms the backbone for any Big Data initiative. Technology components for Big Data would include, Hardware Infrastructure: Organizations need to identify their needs and plan for the hardware infrastructure required for their efforts. This is why Cloud Computing is a great option if you don’t want heavy investments. Software tools: You also need to invest in the right set of tools for collection, processing and storing of data and for deriving analytics and visualization of data. A Jump Start Guide to Big Data on AWS Cloudl26
  25. 25. CHAPTER 5 The Big Data Life Cycle
  26. 26. KINESIS REDSHIFT EMR DYNAMO DB The Big Data Life Cycle is essentially divided into 4 sections, the first of these phases is Getting or generating the data. The Cost of data generation is falling rapidly which has resulted in vast amount of data being generated. Data is generated from multiple sources in multiple different formats. While data generation step happens at different sources, you need to have your own resources to succeed in the following steps; i.e. collect, store, analyze and sharing the data. Traditional hardware and software technologies are not capable of handling such high volumes of data generated in multiple formats. To process Big Data, as we have discussed earlier you require software that is designed for distribution, with easy programming model and independent of the underlying platform. One example of such a software ecosystem is Hadoop. Also vast amounts of hardware infrastructure would be required to process this Big Data where the hardware has to be scalable and distributed. We see a calling for Cloud Computing again. Generation Collection & Store Analyze & Computation Data Collaboration & Sharing A Jump Start Guide to Big Data on AWS Cloudl28
  27. 27. CHAPTER 6 How AWS Components Fit in the Big Data Life Cycle
  28. 28. KINESIS REDSHIFT EMR DYNAMO DB Transferring the Data In Order to begin the ETL (Extract, Transfer & Load) process, you first need to transfer your Data sets onto the Cloud Storage services. You can do this by using any of AWS’s Data Transfer services based on your type, size & criticality of your data. There are 3 main services used, 1. AWS Import/Export: AWS Import/Export allows you to transfer large amounts of data into and out of AWS using portable storage devices that are shipped to AWS data centers directly. AWS transfers your data directly onto and off of storage devices using Amazon’s high-speed internal network bypassing the internet. For significantly large data sets, AWS Import/Export is often faster option than Internet transfer and more cost effective than upgrading your connectivity. AWS Import/Export Move large amounts of data into and out of AWS using portable storage devices for transport A Jump Start Guide to Big Data on AWS Cloudl30
  29. 29. KINESIS REDSHIFT EMR DYNAMO DB 2. AWS Direct Connect: AWS Direct Connect makes it easy to establish a dedicated network connection from your premise directly to AWS. Using AWS Direct Connect, you can establish private connectivity between AWS and your datacenter, office, or colocation environment, which in many cases can reduce your network costs, increase bandwidth throughput, and provide a more consistent network experience than Internet-based connections. 802.1q VLANs, this dedicated connection can be partitioned into multiple virtual interfaces allowing you to use the same connection to access both private & public resources. 3. AWS Storage Gateway: The AWS Storage Gateway is a service that allows you to connect your on-premises software appliances with the cloud-based storage providing a seamless and secure integration between an organization’s on-premises IT environment and AWS’s storage infrastructure. The service allows you to securely store data in the AWS cloud for scalable and cost-effective storage. There are 3 major types of storage gateway configurations, and you can select yours based on the kind of data & operations you want to perform on them. AWS Direct Connect AWS Storage Gateway Establish a dedicated network connection from your premises to AWS Secure Integration between an On- premises IT & AWS’s storage infrastructure A Jump Start Guide to Big Data on AWS Cloudl31 Transferring the Data
  30. 30. KINESIS REDSHIFT EMR DYNAMO DB Collecting and Storing the Data Once the data is Transferred it can be collected & Stored for analytics. A variety of AWS Resources are present to help you collect and store information & based on the type of Data, you can Store it in Simple Storage Components using AWS's S3 service, relational or NoSQL database services. The following are the service components in Detail: 1. AWS Simple Storage Service (S3): Amazon S3 is storage for the Internet. It is designed to make web-scale computing easy and reliable. Amazon S3 provides a simple web services interface that can be used to store and retrieve any amount of data, at any time, from anywhere on the web. Amazon S3 is an ideal way to store large amount of data for analysis because of its reliability and cost effectiveness. Apache Hadoop file systems can be hosted on S3, as its requirements of a file system are met by S3. As a result, Hadoop can be used to run MapReduce algorithms on EC2 servers, reading data and writing results back to S3. Simple Storage Service (S3) Write, read, and delete objects containing from 1 byte to 5 terabytes of data each. A Jump Start Guide to Big Data on AWS Cloudl32
  31. 31. KINESIS REDSHIFT EMR DYNAMO DB 2. Amazon Relational Database Service (RDS): Amazon RDS is a managed service that makes it easy to setup, operate and scale a relational database on AWS infrastructure. AWS RDS currently supports Postgres, MYSQL, Oracle and MS SQL Server relational database technologies. If you require a relational database to store large amount of data, Amazon RDS is a good fit. 3. AWS DynamoDB: DynamoDB is a fully managed NoSQL database service by AWS. DynamoDB is a fast, highly reliable and cost- effective NoSQL database service designed for internet scale applications. It is designed to provide fast performance at any scale. Once you have your Data Stored, it’s time for the Analysis part which we will cover in the next Chapter. A full featured relational databases giving you access to capabilities of a MySQL, Oracle, SQL Server, or PostgreSQL databases engines AWS Relational Database Service (RDS) A fast, fully managed NoSQL database service making it simple & cost-effective to store & retrieve any amount of data, and serve any level of request traffic. AWS DynamoDB A Jump Start Guide to Big Data on AWS Cloudl33 Collecting and Storing the Data
  32. 32. KINESIS REDSHIFT EMR DYNAMO DB Collection & Store Collection & Storage Data Collaboration & Sharing Analyze & Computation AWS Data Pipeline AWS EMR A Jump Start Guide to Big Data on AWS Cloudl34 1 3 2
  33. 33. CHAPTER 7 Hadoop in the Clouds using AWS’s EMR
  34. 34. KINESIS REDSHIFT EMR DYNAMO DB Continuing with the Big Data lifecycle Story, let us get to the core, i.e. the Analysis section. When we talk about Big Data one of the First technologies frequently spoken about is Hadoop, if you want to dive down deep into Hadoop Technologies, you can refer to this Blog post comparing apache Hadoop with the other commonly used Hadoop Technologies. So before we understand what exactly EMR is, let’s keep in mind that the EMR service basically allows us to automate Hadoop Processes on AWS Cloud. So, what is Amazon EMR? Amazon EMR is a managed Hadoop distribution by Amazon Web Services. Amazon EMR helps us analyze & process large amount of data by distributing data computation across multiple nodes in a cluster on AWS Cloud. It uses a customized Apache Hadoop framework to achieve large scale distributed processing of data. Hadoop framework uses distributed data processing architecture known as MapReduce, in which a data processing task is mapped to a set of servers in a cluster for processing. The results of the computation performed by these servers are reduced to a single output. Amazon Elastic Map Reduce (EMR) A Jump Start Guide to Big Data on AWS Cloudl36 Amazon Cloud’s Managed Hadoop!
  35. 35. KINESIS REDSHIFT EMR DYNAMO DB All the open source projects that work with Apache Hadoop also work seamlessly with Amazon EMR. In addition to this Amazon EMR is well integrated with various AWS services like EC2 (Compute Instances used to launch master and slave nodes),S3 (used as an alternative to Hadoop Distributed File Systems –HDFS for Storage), CloudWatch (to monitor jobs on EMR), Amazon RDS, DynamoDB etc. Amazon EMR allows you run your custom map-reduce programs, written in Java. You have the flexibility to launch any number of EC2 instances with various server configurations. EMR also allows you to update the default Hadoop configurations to tune your job flows (job flow is a set of steps to process a specific data set using a cluster of EC2 instances) according to your specific needs. EMR allows writing Bootstrap actions, which provides a way to run custom set-up prior to execution your job flow. Bootstrap actions can be used to install software or configure instances before running a job flow. Overall, Amazon EMR provides a simpler and cost effective way to deploy your own Hadoop cluster without the overheads of buying and maintaining your own hardware and deploying your own Hadoop cluster. . A Jump Start Guide to Big Data on AWS Cloudl37 What Power’s Amazon EMR?
  36. 36. KINESIS REDSHIFT EMR DYNAMO DB Use Cases of Amazon EMR Amazon EMR can be used to process applications with data intensive workloads. Some of the common use case examples for Amazon EMR are: • Data Mining • Log file analysis • Web indexing • Machine learning • Financial analysis • Scientific simulations • Data warehousing • Bioinformatics research Apart from these there could be several specific use cases in your organization that might require large scale data computation, for all such use cases you can use Amazon EMR. USE CASES A Jump Start Guide to Big Data on AWS Cloudl38
  37. 37. KINESIS REDSHIFT EMR DYNAMO DB Major Advantages of EMR • No upfront investments in hardware Infrastructure: • Simple and managed cluster launching • Easy to scale up or down • Integration with other Amazon Web Services including S3 as an alternative to HDFS: • Integration with other Apache Hadoop projects, including Hive and Pig: • Multiple EC2 instance options for clusters gives a lot of flexibility • Integration with leading BI Tools • Multiple management tools including CLI, SDKs and User Console • Limitations of Amazon EMR • Amazon EMR is not open source, so you have limited control over the source code There are increased latencies as typical EMR jobs use data stored in S3 which is processed on EC2, moving data from S3 to EC2 takes some time. Amazon EMR does not support the latest version of Hadoop; current versions supported by EMR are Hadoop 0.20.205 and Hadoop 1.0.3 with custom patches. If your application requires using the latest features of Hadoop, EMR may not be the best option A Jump Start Guide to Big Data on AWS Cloudl39
  38. 38. CHAPTER 8 What are the Big Opportunities with Big Data?
  39. 39. KINESIS REDSHIFT EMR DYNAMO DB The rise of internet and open source software along with Cloud &Mobile computing have ensured that information technology is no longer an advantage for large enterprises nor a bottle neck for SMEs Start Ups. Open Source Software & Cloud Computing have democratized the way Information Technology is being utilized with organizations. With a level playing field created in terms of technology, the next frontier for business leadership is shifting towards the use of vast amount of Information. In the fast changing world of business and technology, information plays an important role in ensuring the success of an organization. In the recent past, the most successful companies have been the ones that have successfully tackled the challenge of Big Data. Big Data is helping organizations of all sizes to make better business decisions, save costs, improve customer service, deliver better user experience, and identify security risks among other things, it is no more a farfetched idea, it is here. So let’s talk about some opportunities that Big Data provides: • Operational Predictability • Improved Customer Service • Managing Security Risks A Jump Start Guide to Big Data on AWS Cloudl41 What are these Opportunities?
  40. 40. KINESIS REDSHIFT EMR DYNAMO DB Operational Predictability Technology has enabled to track operations across different units of business to the most granular level. Collecting and analyzing data from the past can help companies to identify patterns and predict opportunities or issues in a better way. A Jump Start Guide to Big Data on AWS Cloudl42 You can track data generated from machines to sensors to computers to cash registers. Correlating this data can help you get insights in to your operations, real time view of transactions and customer behavior.
  41. 41. KINESIS REDSHIFT EMR DYNAMO DB Improved Customer Service Companies already collect data about their customers in various formats. For a large company consolidating multiple views of the same customer can help in giving a 360-degree view of the existing customers that can further improve customer service. Further to improve on this existing set of data, these customer views can be now expanded to include various internal or external sources of data, for example extracting feed of data about your products from social media. This can help you gain better understanding of customers, on how they perceive your brand vis a vis a competitor or what improvements customers are looking for in your products among other things. A Jump Start Guide to Big Data on AWS Cloudl43
  42. 42. KINESIS REDSHIFT EMR DYNAMO DB Managing Risks Big Data analytics can help with lowering risk of fraud and cyber threats if used correctly. Large banking, financial and insurance companies have implemented various big data solutions that help them identifying investment risks, reducing fraud threats among other things. Governments across the globe have implemented solutions that help them uncover suspicious activities. Of course, not all of these initiatives are well received by everyone, think NSA & Edward Snowden. Organizations also deploy big data to predict online attacks on their cyber assets and mitigate them before they could do any harm. Big Data solutions can really help organizations to solve a whole bunch of problems which were previously difficult if not impossible to resolve. Use of Big Data is not limited to large industries, today SMEs can take equally take advantage of Big Data using Open Source and Cloud Computing technologies. Correct use of Big Data can help SMEs challenge the large incumbents in their industry successfully. Each industry and company has various use cases of Big Data. In the next few posts, I will write about some of these use cases specific to certain industries. A Jump Start Guide to Big Data on AWS Cloudl44
  43. 43. CHAPTER 9 Big Data Use Cases across various Verticals
  44. 44. KINESIS REDSHIFT EMR DYNAMO DB Ring – Ring – Anyone there? You have your Networks Spread over 1000 Cities, some with Over a Million Connections, Telecom Industry faces Challenges such as: • Customer Churning & Retention • Understanding Payment Details for new Schemes • Providing Customized Payment & Service models to the Right Customers • Understand Competitor Pricing Models & faster innovation • Checking Which Offers & Schemes are popular in which Geographies A Jump Start Guide to Big Data on AWS Cloudl46
  45. 45. Operational Efficiency: (10- 15% Open Reduction) Innovative Business Models • Create Data Driven API models for improved customer service. • Using Payment Data from Retail Chains & Outlets to create Coupons & Offers, Combining them with NFC(Near Field Communication) to increase Buying frequency of Customer . • Creating World Class customer care, by tracking in depth subscriber activity, tracking issues and reducing call center iterations & time. • Anticipating & Implementing Network Planning even before the demand & predict Network stress points & Under utilized Network areas. Telecom – Opportunities using Big Data A Jump Start Guide to Big Data on AWS Cloudl47
  46. 46. Precise Business Models Real Time Analysis & Decision Making: (Revenue Potential Inc. 5-10%) • Optimizing Offers based on Subscriber Network usage patterns & Traffic to come up with newer offers which is critical on driving value added service adoption. • Help Service providers understand what behaviors will trigger churn events & what actions will prevent churning, by dynamic offers created by complaint triggers in real time. Reducing Churn Rates by 8-12% . • Controlling RAN(Radio Access Network) Congestion by dividing Subscribers to Individual Sub Cell Levels & by assimilating data of past geographic positions & real time data on current locations can provide priority to certain subscribers over others. • Cyber Cop Initiatives where pattern matches of subscriber activities can be used to detect malicious activities and determine traffic changing abnormal consumptions to predict Fraud activities. A Jump Start Guide to Big Data on AWS Cloudl48 Telecom – Opportunities using Big Data
  47. 47. KINESIS REDSHIFT EMR DYNAMO DB What are You Buying Today? - RETAIL In retail, big data generally refers to the use of sets of information from inside and outside a company that can then be analyzed and used to improve profits. Imagine Your Retail Outlets over a 100s of cities, with more than 10 outlets per city. Even if you have 10,000 customers per month buying at a frequency of 5, you will end up with 5, 00, 00,000 unique records! This Does not even take into Consideration the Age, Gender, Geographic Trend, Weather, Time of the month & more! Challenges faced by Retail Industry are : •Buying Patterns •Shopping Offers •Cross Selling Success •Loyalty & Retention •Effective Marketing Campaigns •Predictive Demands •Dynamic Price Optimization A Jump Start Guide to Big Data on AWS Cloudl49
  48. 48. Personalized Recommendations • Data Collected based on previous online & offline purchases, even online clicks, likes & wish lists are recorded to generate recommendations in real time. • Online Shoppers are given recommendations at reduced prices based on their previous purchase trends. has increased their sales volumes by 25% on this. Dynamic Pricing • Online Shoppers are given reduced prices based on data on time of the day, Festive offers validity period, loyalty of customers and more. • Offline shoppers use these data driven approaches to map shopping patterns and based on proximity of customers allow price variations with RFID price tags. Retail – Opportunities using Big Data A Jump Start Guide to Big Data on AWS Cloudl50
  49. 49. In Store Experience • Geo-Fencing which allows retailers to provide real time offers to customers on their cell phones(based on their previous shopping sprees) as they enter a geo fenced area. • Optimized Product Placement is done Scientifically where algorithms check buyer tendencies & have products placed in the right geography, this becomes very important for bigger players like Wal-Mart & Macy's. Micro Segmentation & Inventory Management • Segmenting Customers have been taken to the next level, with social media interactions, marketing campaign results, wish lists. Targeted offers are now made to granular customer segments with promo codes & coupons! • With Big Data Retailers can get predictive analytics on prices as they fluctuate through the supply chain. This allows them to set prices, and also react proactively to demand spikes to avoid over stock-outs. A Jump Start Guide to Big Data on AWS Cloudl51 Retail – Opportunities using Big Data
  50. 50. KINESIS REDSHIFT EMR DYNAMO DB Will I Get Money for this Accident? - INSURANCE What is the most challenging business issue for insurers today, and what role does analytics play in addressing it? Currently only the largest insurers have traditional policy rating and claims data that would be considered “big” (i.e. potentially unmanageable with a relational database). But insurance companies of all sizes are encountering big data from new sources such as their website traffic, vehicle telematics programs, and social media. Some Big Data Facts about Insurance Industry are: $80 Billion Loss in U.S. per Year due to Frauds! 15% of premium costs in South Africa are Frauds! Claims on Automobiles have a 25-33% Fraud ! • What Policies are best Suited for Customers? • How to tackle Increasing Diseases & ailments? • How to reduce customer Hassles? A Jump Start Guide to Big Data on AWS Cloudl52
  51. 51. Fraud Detection • Turning the Claim Centric Approach Person Centric. Using Cohort analytics to track Social activities of beneficiary & associated parties. • Integration of these Data streams of Information to detect fraud patterns for Future (predictive analysis) . Delighting the Customer • Variety of Customer records can be stored in No SQL databases, and real time integration to multiple sources to optimize the process of the insurance validation & reimbursements. • Customer Call Logs & interaction with staff can be checked for Sentiment analysis, to optimize insurance processes & reduce iterations base on negative customer calls. Insurance – Opportunities using Big Data A Jump Start Guide to Big Data on AWS Cloudl53
  52. 52. Predictive Analysis Understand Customer Lifestyle by integrating feeds of social networks to determine Disease Patterns so that insurance schemes 10-15 years into the future insurance companies can identify these degenerating lifestyles & offer schemes at higher premiums. Improving Product Opportunities Checking out the Success of Insurance Schemes & Which are most popular, to drive similar scheme models & understand why other schemes are not popular. This can be done by mapping the successful customer base lifestyle trends. A Jump Start Guide to Big Data on AWS Cloudl54 Insurance – Opportunities using Big Data
  53. 53. KINESIS REDSHIFT EMR DYNAMO DB I Want a Smart CAR too! -AUTOMOTIVE In the automotive industry that ranges from your vehicle’s driving data to analyzing how efficient an assembly line worker’s movements are when assembling a vehicle. “Big data” is one of the key points and opportunities for automakers regarding information technology solutions. Some Big Data Facts about Automotive Industry are: • Number of Internet Capable Vehicles in Europe will be 48Mil by 2016! • There are more than 74 Sensors in Ford’s Connected Cars! • These Hybrid Cars can generate 25GBs/Hour of Data!! A Jump Start Guide to Big Data on AWS Cloudl55
  54. 54. Vehicle Insurance • Using "Telematics", driver's driving patterns can be analyzed. These can be used by insurance companies to give out alerts, warnings in real time & even give personalized pricing. Integrating with Geo Fencing & Social Media • GPS trackers can provide customized alerts as vehicles pass a particular location. These alerts are real time & based on your social media likes & shared combined with discount coupons & offers! Automotive – Opportunities using Big Data A Jump Start Guide to Big Data on AWS Cloudl56
  55. 55. Self Repair & Maintenance • Your Car's intelligence system will keep a track of all parts & liquids to be changed or repaired for periodic maintenance, giving you real time alerts as you pass repair shops, which will also bid for discounted pricing! Learning from the Mistakes • Using The Black Box mechanism similar to airplanes, product engineers can understand if any vehicle part was the cause & how parts can be improved in design to reduce future accidents. A Jump Start Guide to Big Data on AWS Cloudl57 Automotive – Opportunities using Big Data
  56. 56. KINESIS REDSHIFT EMR DYNAMO DB My Favorite CARTOON is ON! - MEDIA Among the big data issues M&E executives cite: “Poor data reliability, inability to integrate data from different customer touch points and a shortfall in the skills and tools needed to derive useful insights from customers as they click, engage or turn away from the content and services being offered.” Challenges – The Media Industry • What Content is popular? • Where are my viewers coming from? • What are my viewer’s opinions about my content? • How Do I monetize my Content? A Jump Start Guide to Big Data on AWS Cloudl58
  57. 57. • Using Big Data Tools to analyze current content viewed, the storylines, characters etc. to determine which type of movies and or soaps are going to be a success in the future. • Analyzing Viewer demographics, the popular content, devices used to view and download data, detecting spams, browsers and OS used to generate actionable reports driving business decisions. Media – Opportunities using Big Data A Jump Start Guide to Big Data on AWS Cloudl59
  58. 58. • Tracking user comments, likes, shares, tweets & other user interactions with media content on social networks to track popularity & promote content similar to the hits. Website Optimization • Based on Navigational pattern analysis that are popular among users, website builders can optimize the ease of reach of the content. Ad Targeting & Scope to Monetize • Ad servers based on Visitor Cookie analysis & bid values generated sometimes even in real time, deliver ads to visitors in real time & continuously update for successful clicks & failures. A Jump Start Guide to Big Data on AWS Cloudl60 Media – Opportunities using Big Data
  59. 59. KINESIS REDSHIFT EMR DYNAMO DB What’s the Interest Rate? - BANKING Banks that can harness big data, in the form of transactions, real-time market feeds, customer-service records, correspondence and social media posts, can derive more insight about their business than ever before and build competitive advantage. Successfully harnessing big data can help banks achieve three critical objectives for banking transformation: Create a customer-focused enterprise Optimize enterprise risk management Increase flexibility and streamline operations Challenges – The Banking Industry •Detecting Frauds •Which Schemes for which customers? •When is my Customer Not Happy? •How Do I Segment my customer base? A Jump Start Guide to Big Data on AWS Cloudl61
  60. 60. Creating Customer Segmentation • Banks are now pooling in all types of customer buying patterns, lifestyle habits & interests to create segmentations. With this 360 degree view from Big Data Analytics integration, banks can now customize product offerings & re distribute spending from non profitable to profitable customers. Fraud Detection • Financial & Banking Institutes are using credit/debit card purchases to understand spending habits and detect suspicious patterns of buying to detect frauds. Banking – Opportunities using Big Data A Jump Start Guide to Big Data on AWS Cloudl62
  61. 61. • Banks and Financial Institutions can now track the success or failure of their product or schemes as they integrate social sentiments of their products and track user complaints. Sales & Marketing Campaigns • Using 360 Degree Customer Insights banks are generating smarter marketing & sales campaigns integrating them with offers & schemes that are more successful to the different customer segments. Analyzing Voice Sentiments • Many Banks are now using highly unstructured data, such as customer voices, and using complex data analysis to track customer complaints . They are also trying to integrate the information with the transactional data warehouse to reduce attrition, drive up sales & even detect frauds. A Jump Start Guide to Big Data on AWS Cloudl63 Banking – Opportunities using Big Data
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