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Rick Mutsaers Informatica
- 1. ` 20.09.17 Building intelligent data lakesRick Mutsaers Specialist Big Data Technologies
- 2. 2 © Informatica. Proprietary and Confidential. New Questions Are Forcing Data-Driven Digital Transformation Engage Your Customers Optimize Your Operations Transform Your Products Empower Your People Digital Transformation Goals What specific interactions and events led to conversion? How can identify new sources of cross-sell and upsell revenue? What specific patterns affect employee retention or patient outcomes? How can we improve the productivity of employees? What specific events and patterns are fraudulent and anomalous? How can minimize cost and increase profitability? What specific signals are there to better predictive downtime and failure? How improve the uptime of key processes to improve products and services?
- 3. 3 © Informatica. Proprietary and Confidential. There is a Generational Market Disruption Underway Databases Azure HDInsight Compute Analytics Storage Tabl es Applications Proprietary Engines Strea ms Pig Azure Storage
- 4. 4 © Informatica. Proprietary and Confidential. Data 3.0 brings in new challenges Data Volume & Velocity New Users 325 million business data users and growing New Data Types (mobile, social, IoT) 20 billion connected devices Data in the Cloud Over 92% of data center traffic will come from the Cloud AI driven Apps & Analytics 1 billion workers will be assisted by machine learning or AI 600 terabytes of Facebookdataperday
- 5. 5 © Informatica. Proprietary and Confidential. Key capabilities of modern data lake architectures for Data 3.0 Integrate all types of structured & unstructured data Process dynamically changing data Connect to on- premises & Cloud data Deploy on multiple cloud platforms Automate data flows into data hungry apps Guide behavior with intelligent recommendations Catalog all data Collect and process batch & streaming data with big data scale Provide self- service data preparation Subscribe to certified data Data Volume & Velocity New Users New Data Types (mobile, social, IoT) Data in the Cloud AI driven Apps & Analytics
- 6. 6 © Informatica. Proprietary and Confidential. Typical, fragmented approach does not scale INGEST BIG DATA INFRASTRUCTURETRADITIONAL INFRASTRUCTURE Aquire Ingest Hand-coding INGEST Prepare Hand-coding INGEST Secure Hand-coding INGEST Master Hand-coding INGEST Govern INGEST Serve Hand-coding Consume
- 7. 7 © Informatica. Proprietary and Confidential. Data is gold Metadata is the diamond in the rough
- 8. Usage Operational Technical Business Types of metadata to be collected
- 9. 9 © Informatica. Proprietary and Confidential. Recommendation Build a data catalog
- 10. Recommendation Abstraction layer is protection against technology changes Don’t start handcoding !
- 11. Recommendation Choose a tightly integrated platform
- 12. 12 © Informatica. Proprietary and Confidential. more than 700 customers incl. over 400 mobile data customers top 3 voice carrier with over 28 bio minutes world leader in mobile data services 1.65 bio euro revenues HQ in Brussels with offices in Bern, Dubai, Singapore and New York 400+ employees 22.4% 20%57.6% Customer example: BICS
- 13. 13 © Informatica. Proprietary and Confidential. Provide a 360 view on Roaming Activity PROJECT THE
- 14. 14 © Informatica. Proprietary and Confidential. The Roadmap • Phase 1 : Migrate the data storage and processing from the Teradata DB to the Hybrid Platform Set up the loading of the data into hadoop Move all the Tracking Applications (using the detailed data) into Hadoop and keep the SLA (<1 min) for the Subscriber Tracking Move the processing of all the high latencies (15 minutes, Hourly, Daily) application to Hadoop • Phase 2: Compute the new analytics in realtime on Hadoop and provide longer historical reporting to customers
- 15. ` Learn more: Visit us at stand 102 Thank you ! Rick Mutsaers +31 622 414240 rmutsaers@informatica.com
Notas del editor
- So what is changing in the world of Analytics. First of all we see new questions being asked to be able to control and grow business. Like what interaction did we have with our customer that pursuaded him/her to buy our product. Or in healthcare, what did we do to make the patient better. Or in general, do we understand the reasons why employees leave our company, so we can prevent this in future. But also what can we do to lower cost and optimize our business performance.
- The infrastructure choices to help with these questions have dramatically changed over the past decade.
- And this new era we call Data 3.0 where data is used as a strategic asset to fuel this disruption comes with new challenges. Like data volumes, and types of data. or the fact that data consumers need smarter applications, meaning AI and Machine Learning. And we see a whole new group of data consumers. Finally we see a big shift toward cloud. So how do you integrate data that’s not in your datacenter. More specifically that’s spread across many cloud applications.
- Looking at these challenges, what fo we then need in terms of capabilities...
- But don’t make the mistake of starting to use a plethora of non-integrated tools to tackle these problemns, rather think of an integrated platform that provides all the capabilities you need.
- Now the new adagium is ‘Data is the new gold’. Then i would pose the statement ‘Metadata is the diamond in the rough’. Let me explain.
- There are different types of metadata we need to collect to face these challenges
- Using all this metadata create a data catalog that business users can query to understand what data there is, who owns it, what it means, what quality it has etc.
- This can also be used to create data integration patterns based on an abstracted view
- The benefit of using a tightly integrated platform are numerous....
- Now lets look at a customer example that has gone through this digital revolution. BICS is a network provider for roaming services.
- They started a project to get better view on roaming activity to better predict network usage, disruptions, customer behavior. To do this they
- To do this they first implemented a Hadoop platform to offload data from expensive Teradata into cheaper HDFS and then moved some of their existing data integration logic (built using Informatica’s PowerCenter data integration suite) to Hadoop for improved performance and scalability. They moved most of the batch processing to Hadoop now leveraging the technology benefits of that platform. They started with MapReduce and have now switched to Spark, without recoding. Next phase will be to also start to process data in realtime/streaming mode to get even lower latencies for predictive maintenance and quicker response to disruptions.