Build a Successful Data Lake with a Smart Data Catalog
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This document discusses how to build a successful data lake by focusing on the right data, platform, and interface. It emphasizes the importance of saving raw data to analyze later, organizing the data lake into zones with different governance levels, and providing self-service tools to find, understand, provision, prepare, and analyze data. It promotes the use of a smart data catalog like Waterline Data to automate metadata tagging, enable data discovery and collaboration, and maximize business value from the data lake.
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Build a Successful Data Lake with a Smart Data Catalog
- 1. How to Build a Successful Data Lake Alex Gorelik Waterline Data Founder and CEO
- 2. Data Lakes Power Data-Driven Decision Making
- 3. Maximize Business Value With a Data Lake How Do You Democratize the Data Lake to Maximize Business Value? Data Lake Data Puddle Data Swamp No Value Enterprise Impact Tight Control “Governed” Self-Service Business Value Data Democratization DW Off- loading
- 4. Data Swamps Raw data Can’t find or use data Can’t allow access without protecting sensitive data
- 5. Data Warehouse Offloading: Cost Savings I prefer a data warehouse--it’s more predictable It takes IT 3 months of data architecture and ETL work to add new data to the data lake I can’t get the original data
- 6. Low variety of data and low adoption • Focused use case (e.g., fraud detection) • Fully automated programs (e.g., ETL off-loading) • Small user community (e.g., data science sand box) Strong technical skill set requirement Data Puddles: Limited Scope and Value
- 7. What Makes a Successful Data Lake? Right Data Right InterfaceRight Platform + +
- 8. Right Platform: • Volume—Massively scalable • Variety—Schema on read • Future proof—modular—same data can be used by many different projects and technologies • Platform cost – extremely attractive cost structure
- 9. Right Data Challenges Most Data is Lost, So it Can’t Be Analyzed Later Only a small portion of data in enterprises today is saved in data warehouses Data Exhaust
- 10. Right Data: Save Raw Data Now to Analyze Later • Don’t know now what data will be needed later • Save as much data as possible now to analyze later
- 11. • Don’t know now what data will be needed later • Save as much data as possible now to analyze later • Save raw data, so it can be treated correctly for each use case Right Data: Save Raw Data Now to Analyze Later
- 12. • Departments hoard and protect their data and do not share it with the rest of the enterprise • Frictionless ingestion does not depend on data owners Right Data Challenges: Data Silos and Data Hoarding
- 13. Right Interface: Key to Broad Adoption • Data marketplace for data self-service • Providing data at the right level of expertise
- 14. Providing Data at the Right Level of Expertise Data scientists Business analysts Raw data Clean, trusted, prepared data
- 15. Roadmap to Data Lake Success Organize the lake Set up for self-service Open the lake to the users
- 16. Organize the Data Lake into Zones Organize the lake
- 17. Multi-modal IT – Different Governance Levels for Different Zones Raw or Landing Sensitive Gold or Curated Work Data Stewards Data Scientists Data Engineers Data Scientists, Business Analysts Minimal governance Make sure there is no sensitive data Minimal governance Make sure there is no sensitive data Heavy governance Trusted, curated data Lineage, data quality Heavy governance Restricted access
- 18. Business Analyst Self-Service Workflow Find and Understand Provision Prep Analyze Set up for self-service
- 19. Finding, understanding and governing data in a data lake is like shopping at a flea market “We have 100 million fields of data – how can anyone find or trust anything?” – Telco Executive
- 20. Botond Horvath / Shutterstock.com DATA SCIENTIST / BUSINESS ANALYST DATA STEWARD BIG DATA ARCHITECT Can’t govern and trust data (unknown metadata, data quality, PII, data lineage) Need data to use with self- service tools but can’t explore everything manually to find and understand data Can’t catalog all the data manually and keep up with data provisioning
- 21. Instead Imaging Shopping On Amazon.com Catalog Find, Understand And Collaborate Provision
- 22. Catalog Find, Understand And Collaborate Provision Waterline Data is like Amazon for Data in Hadoop
- 23. Finding and Understanding Data • Crowdsource metadata and automate creation of a catalog • Institutionalize tribal data knowledge • Automate discovery to cover all data sets • Establish trust • Curated annotated data sets • Lineage • Data quality • Governance Find and Understand
- 24. Accessing and Provisioning Data You cannot give all access to all users You must protect PII data and sensitive business information Provision Agile/Self-service approach Create a metadata-only catalog When users request access, data is de-identified and provisioned Top down approach Find and de-identify all sensitive data Provide access to every user for every dataset as needed
- 25. Provide a Self-Service Interface to Find, Understand, and Provision Data
- 26. Prepare data for analytics Prep Clean data Remove or fix bad data, fill in missing values, convert to common units of measure Shape data Combine (join, concatenate) Resolve entities (create a single customer record from multiple records or sources) Transform (aggregate, bucketize, filter, convert codes to names, etc.) Blend data Harmonize data from multiple sources to a common schema or model Tooling Many great dedicated data wrangling tools on the horizon Some capabilities in BI and data visualization tools SQL and scripting languages for the more technical analysts
- 27. Data Analysis • Many wonderful self- service BI and data visualization tools • Mature space with many established and innovative vendors Magic Quadrant for Business Intelligence and Analytics Platforms 04 February 2016 | ID:G00275847 Analyst(s): Josh Parenteau, Rita L. Sallam, Cindi Howson, Joao Tapadinhas, Kurt Schlegel, Thomas W. Oestreich Analyze
- 28. Unlock the Value of the Data Lake with the Waterline Data Smart Data Catalog Time To Value Tribal Knowledge Sharing Trust
- 29. Waterline Data Is The Only Smart Data Catalog For The Data Lake “Use an INFORMATION CATALOG TO MAXIMIZE BUSINESS VALUE From Information Assets” “automatically identify, profile, and metatag files in HDFS and make them available for analysis and exploration” “tapped into an important and underserved opportunity” “comprehensive big data governance and discovery platform” “opens the data to a wider variety of people” “fills a critical gap in big data exploratory analytics by automating the tagging and cataloging of data”
- 30. Current Customers Healthcare Insurance Life Sciences Aerospace Automotive Banking Government Marketing "Opening up a data lake for self-service analytics requires a data catalog that's smart enough to automatically catalog every field of data so business analysts can maximize time to value” -- Jerry Megaro, Global Head Of Data Analytics, Merck KGaA “Understanding where your data came from and what it means in context is vital to making a data lake initiative successful and not just another data quagmire – the catalog plays a critical component in this” -- Global Head of Data Governance, Risk, and Standard, International Multi-Line Insurer “A governed yet agile data catalog is key to open up the data lake to business people” -- Paolo Arvati, Big Data, CSI- Piemonte
- 31. We Run Natively On Hadoop And Integrate With Existing Tools
- 32. Workflow of Enabling Self-Service Analytics With Hortonworks Hortonworks Atlas And Ranger Data Prep Analytics & Visualization Smart Data DiscoveryProfiling, Sensitive Data & Data Lineage Discovery, Automated Tagging Data Stewardship Curate Tags Self-Service Data Catalog Find, Collaborate And Take Action Metadata, Tags, Data Lineage Metadata, Tags, Roles & Access Control Roles & Access Control
- 33. A Successful Data Lake Right Data Right InterfaceRight Platform + +
- 34. Come to Booth 303 to see a demo and talk to us about your data lake Come to the Atlas session at 4:00 PM on Thursday in room 210C
- 35. Waterline Data The Smart Data Catalog Company
Notes de l'éditeur
- End-user tools only provide the last mile to leverage data, but they of and by themselves don’t know where the right data is. The right data has to be found, quickly and securely.
- The opposite of a flea market is Amazon. It gives the consumer self-service, but it functions as a managed application.
- Like Amazon, we offer a solution that catalogs the data assets, provides a front-end to find, understand, and share, and provides a way to take action and quickly open the data in any end-user tool to wrangle, visualize, or analyze the data.
- A data lake provides one place where any data can be saved and used by business analysts and data scientists to mash up data in new ways to answer new business questions. Waterline Data enables you to open up the data lake to business analysts and data scientists so they can do data prep, analytics, or modeling. Our product delivers value along 3 dimensions (i.e., the 3 T’s). We catalog every field of data for the entire data lake and we provide an interface to quickly find, understand, and take action on the data (e.g., you can provision or open the data in Trifacta) – The end result is faster time to uncover value We don’t just discover what the data means, but we also empower subject matter experts to augment the data catalog with additional tags and comments to capture additional information, such as the intended use of the data, to help accelerate future projects We facilitate data governance by tagging data based on approved business glossaries and data stewardship curation, as well as by providing secure self-service access to the data based on roles and visibility rules
- Waterline Data has been acknowledged as filling an important gap in opening up data lakes for self-service data preparation and analytics. The need for a data catalog has been recognized as key to enabling a data democracy and self-service by the business. For instance Gartner just released a paper on how CDOs can leverage an information catalog to get more business value from data assets. We are the only company that can build a data catalog automatically, and at scale, for a data lake.
- We have customers in production across many industries. They realize value by being able to catalog all the data quickly and make it easily available to the business to do self-service data preparation and analytics. They also get value from the fact that the data catalog supports agile data governance, by enabling data stewards to quickly curate tags, and by providing several levels of access control based on the data governance policies (e.g., access to sensitive data is protected). (if they ask, data lakes range from smaller 5-node clusters to over 100 nodes, so our product can be used right away even when the lake is small, and grow to a large lake)
- Our product runs natively on the major platforms like AWS, Cloudera, Hortonworks, MapR, and Pivotal. We are also in the process of certifying on IIP. We integrate with existing data management tools: We can import and export data lineage and tag information with Atlas and Navigator We support access control policies and integrate with LDAP, Ranger and Sentry We can import existing business glossaries from Collibra, Informatica, or IBM (note this is done through our API so we should be able to import from any business glossary) We can integrate with ETL tools to import metadata We integrate with end-user tools through an open framework (we provide the ability to generate Hive tables automatically, as well as the ability to open the data directly in end-user tools)
- Waterline Data accelerates the creation of the data catalog at big data scale: We parse, profile, and discover sensitive data and data lineage, and automatically tag fields based on an integrated business glossary and tagging rules We empower data stewards to quickly curate tags We empower business analysts and data scientists to quick find the right data they need and take immediate action with the data by being able to open it with the desired end-user tool