Achieve data democracy in data lake with data integration
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Saurabh K. Gupta discusses achieving data democratization through effective data integration. He outlines key considerations for data lake architecture and data ingestion frameworks to implement a data lake that empowers data democratization. The document provides an overview of data lake styles, principles for data ingestion, and techniques for batched and streaming data integration like Apache Sqoop, Apache Flume, and change data capture.
![Sqoop - FYI
Design considerations - I
• Mappers
• --num-mappers [n] argument
• run in parallel within Hadoop. No formula but needs to be judiciously set
• Cannot split
• --autoreset-to-one-mapper to perform unsplit extraction
• Source has no PK
• Split based on natural or surrogate key
• Source has character keys
• Divide and conquer! Manual partitions and run one mapper per partition
• If key value is an integer, no worries
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Achieve data democracy in data lake with data integration
- 1. Achieve Data Democratization with effective Data Integration Saurabh K. Gupta Manager, Data & Analytics, GE www.amazon.com/author/saurabhgupta @saurabhkg
- 2. Disclaimer: “This report has been prepared by the Authors who are part of GE. The opinions expresses herein by the Authors and the information contained herein are in good faith and Authors and GE disclaim any liability for the content in the report. The report is the property of GE and GE is the holder of the copyright or any intellectual property over the report. No part of this document may be reproduced in any manner without the written permission of GE. This Report also contains certain information available in public domain, created and maintained by private and public organizations. GE does not control or guarantee the accuracy, relevance, timelines or completeness of such information. This Report constitutes a view as on the date of publication and is subject to change. GE does not warrant or solicit any kind of act or omission based on this Report.”
- 3. AIOUG Sangam’17 ✓ Data lake is relatively a new term when compared to all fancy ones, since the industry realized the potential of data. ✓ Enterprises are bending their backwards to build a stable data strategy and take a leap towards data democratization. ✓ Traditional approaches pertaining to data pipelines, data processing, data security still hold true but architects need to scoot an extra mile while designing a big data lake. ✓ This session will focus on data integration design considerations. We will discuss the relevance of data democratization in the organizational data strategy. Abstract ✓ Data Lake architectural styles ✓ Implement data lake for democratization ✓ Data Ingestion framework principles Learning objectives ✓ Manager, Data & Analytics at General Electric ✓ 11+ years of experience in data architecture, data engineering, analytics ✓ Books authored: ✓ Practical Enterprise Data Lake Insights | Apress | 2018 ✓ Advanced Oracle PL/SQL Developer’s Guide | Packt | 2016 ✓ Oracle Advanced PL/SQL Developer Professional Guide | Packt | 2012 ✓ Speaker at AIOUG, IOUG, NASSCOM ✓ Twitter @saurabhkg ✓ Blog@ sbhoracle.wordpress.com About Me
- 4. Practical Enterprise Data Lake Insights - Published Footer Handle Data-Driven Challenges in an Enterprise Big Data Lake Use this practical guide to successfully handle the challenges encountered when designing an enterprise data lake and learn industry best practices to resolve issues. What You'll Learn: • Get to know data lake architecture and design principles • Implement data capture and streaming strategies • Implement data processing strategies in Hadoop • Understand the data lake security framework and availability model Apress – https://www.apress.com/in/book/9781484235218/ Amazon - https://www.amazon.com/Practical-Enterprise-Data-Lake- Insights/dp/1484235215/
- 5. Data Explosion Future data trends AIOUG Sangam’17 Data as a Service Cybersecurity Augmented Analytics Machine Intelligence “Fast Data” and “Actionable data”
- 6. Evolution of Data Lake • James Dixon’s “time machine” vision of data • Leads Data-as-an-Asset strategy “If you think of a datamart as a store of bottled water – cleansed and packaged and structured for easy consumption – the data lake is a large body of water in a more natural state. The contents of the data lake stream in from a source to fill the lake, and various users of the lake can come to examine, dive in, or take samples.” -James Dixon Footer
- 7. July 21, 2018 Presentation Title 7 Conceptualizing a Data Lake © 2018 Gartner, Inc. Data Lake Conceptual Design Insight Discovery and Development Data Acquisition Analytics Consumption Optimization and Governance © 2018 Gartner, Inc. Data Lake Input, Processing and Output Data In Acquisition Data Out ConsumptionDiscovery and Development Metadata, Modeling and Governance Data Lake Tooling and Infrastructure 01011 Extend analytical capabilities to unrefined data in its native or near-native format Architecture of an Enterprise Data Lake depends upon Data-In and Data-Out strategy
- 8. July 21, 2018 Presentation Title 8 Data Lake Architecture Styles © 2018 Gartner, Inc. Outflow Data Lake 10101 01010 10101 Data Science Lab Inflow Data Lake “Data Hub” to bridge information silos to find new insights Under agile governance, caters data to the consumers at a rapid scale With minimal governance, enables innovation and drives analytics Analytics as a Service
- 9. Data Democracy Provide the data in a matter in which it can be consumed - regardless of the people, process or end user technology Footer Data Empowerment Complement business with data Data Commercialization Data backed decisions Data As A Service Data Vision
- 10. Enterprise Data Lake operational pillars Footer Data Lake Data Management Data Ingestion Data Engineering Data Consumption Data Integration
- 12. Understand the data • Structured data is an organized piece of information • Aligns strongly with the relational standards • Defined metadata • Easy ingestion, retrieval, and processing • Unstructured data lacks structure and metadata • Not so easy to ingest • Complex retrieval • Complex processing Footer
- 13. Understand the data sources • OLTP and Data warehouses – structured data from typical relational data stores. • Data management systems – documents and text files • Legacy systems – essential for historical and regulatory analytics • Sensors and IoT devices – Devices installed on healthcare, home, and mobile appliances and large machines can upload logs to data lake at periodic intervals or in a secure network region • Web content – data from the web world (retail sites, blogs, social media) • Geographical data – data flowing from location data, maps, and geo-positioning systems. Footer
- 14. Data Ingestion Framework Design Considerations • Data format – What format is the data to be ingested? • Data change rate – Critical for CDC design and streaming data. Performance is a derivative of throughput and latency. • Data location and security – • Whether data is located on-premise or public cloud infrastructure. While fetching data from cloud instances, network bandwidth plays an important role. • If the data source is enclosed within a security layer, ingestion framework should be enabled establishment of a secure tunnel to collect data for ingestion • Transfer data size (file compression and file splitting) – what would be the average and maximum size of block or object in a single ingestion operation? • Target file format – Data from a source system needs to be ingested in a hadoop compatible file format. Footer
- 15. ETL vs ELT for Data Lake • Heavy transformation may restrict data surface area for data exploration • Brings down the data agility • Transformation on huge volumes of data may foster a latency between data source and data lake • Curated layer to empower analytical models Footer ETL ELT
- 16. Batched data ingestion principles Structured data • Data collector fires a SELECT query (also known as filter query) on the source to pull incremental records or full extract • Query performance and source workload determine how efficient data collector is • Robust and flexible Footer • Change Track flag – flag rows with the operation code • Incremental extraction – pull all the changes after certain timestamp • Full Extraction – refresh target on every ingestion run Standard Ingestion techniques
- 17. Change Data Capture • Log mining process • Capture changed data from the source system’s transaction logs and integrate with the target • Eliminating the need to run SQL queries on source system. Incurs no load overhead on a transactional source system. • Achieves near real-time replication between source and target Footer
- 18. Change Data Capture Design Considerations • Source database be enabled for logging • Commercial tools - Oracle GoldenGate, HVR, Talend CDC, custom replicators • Keys are extremely important for replication • Helps capture job in establishing uniqueness of a record in the changed data set • Source PK ensures the changes are applied to the correct record on target • PK not available; establish uniqueness based on composite columns • Establish uniqueness based on a unique constraint - terrible design!! • Trigger based CDC • Event on a table triggers the change to be captured in an change-log table • Change-log table merged with the target • Works when source transaction logs are not available Footer
- 19. LinkedIn Databus CDC capture pipeline • Relay is responsible for pulling the most recent committed transactions from the source • Relays are implemented through tungsten replicator • Relay stores the changes in logs or cache in compressed format • Consumer pulls the changes from relay • Bootstrap component – a snapshot of data source on a temporary instance. It is consistent with the changes captured by Relay • If any consumer falls behind and can’t find the changes in relay, bootstrap component transforms and packages the changes to the consumer • A new consumer, with the help of client library, can apply all the changes from bootstrap component until a time. Client library will point the consumer to Relay to continue pulling most recent changes Footer Relay LogWriter Log Storage LogApplier Snapshot Storage Consolidated changes Consistent Snapshot
- 20. Change merge techniques Design Considerations Footer Exchange Partition Prepare final dataset with merged changes P1 P2 P3 P4 #Changes P4 1 Change capture 2 Pull most recent partition for Compare andMerge 3 4 Data source Hive Table with partitions Flow • Table partitioned on time dimension • Changes are captured incrementally • Changes tagged by table name and the most recent partition • Exchange partition process - recent partition compared against the “change” data set for merging
- 21. Apache Sqoop • Native member of Hadoop tech stack for data ingestion • Batched ingestion, no CDC • Java based utility (web interface in Sqoop2) that spawns Map jobs from MapReduce engine to store data in HDFS • Provides full extract as well as incremental import mode support • Runs on HDFS cluster and can populate tables in Hive, HBase • Can establish a data integration layer between NoSQL and HDFS • Can be integrated with Oozie to schedule import/export tasks • Supports connectors to multiple relational databases like Oracle, SQL Server, MySQL Footer
- 22. Sqoop architecture • Mapper jobs of MapReduce processing layer in Hadoop • By default, a sqoop job has four mappers • Rule of Split • Values of --split-by column must be equally distributed to each mapper • --split-by column must be a primary key • --split-by column should be a primary key Footer
- 23. Sqoop - FYI Design considerations - I • Mappers • --num-mappers [n] argument • run in parallel within Hadoop. No formula but needs to be judiciously set • Cannot split • --autoreset-to-one-mapper to perform unsplit extraction • Source has no PK • Split based on natural or surrogate key • Source has character keys • Divide and conquer! Manual partitions and run one mapper per partition • If key value is an integer, no worries Footer
- 24. Sqoop - FYI Design considerations - II • If only subset of columns is required from the source table, specify column list in --columns argument. • For example, --columns “orderId, product, sales” • If limited rows are required to be “sqooped”, specify --where clause with the predicate clause. • For example, --where “sales > 1000” • If result of a structured query needs to be imported, use --query clause. • For example, --query ‘select orderId, product, sales from orders where sales>1000’ • Use --hive-partition-key and --hive-partition-value attributes to create partitions on a column key from the import • Delimiters can be handled through either of the below ways – • Specify --hive-drop-import-delims to remove delimiters during import process • Specify --hive-delims-replacement to replace delimiters with an alternate character Footer
- 25. Oracle copyToBDA • Licensed under Oracle BigData SQL • Stack of Oracle BDA, Exadata, Infiniband • Helps in loading Oracle database tables to Hadoop by – • Dumping the table data in Data Pump format • Copying them into HDFS • Full extract and load • Source data changes • Rerun the utility to refresh Hive tables Footer
- 26. Greenplum’s GPHDFS • Setup on all segment nodes of a Greenplum cluster • All segments concurrently push the local copies of data splits to Hadoop cluster • Cluster segments yield the power of parallelism Footer Writeable Ext Table Writeable Ext Table Writeable Ext Table Writeable Ext Table Writeable Ext Table Hadoop clusterGreenplum cluster
- 27. Stream unstructured data using Flume • Distributed system to capture and load large volumes of log data from different source systems to data lake • Collection and aggregation of streaming data as events Footer Incoming Events Outgoing Data Source Sink Flume Agent Channel Source Transaction Sink Transaction Client PUT TAKE
- 28. Apache Flume - FYI Design considerations - I • Channel type • MEMORY - events are read from source to memory • Good performance, but volatile. Not cost effective • FILE – events are ready from source into file system • Controllable performance. Persistent and Transactional guarantee • JDBC - events are read and stored in Derby database • Slow performance • Kafka – store events in Kafka topic • Event batch size - maximum number of events that can be batched by source or sink in a single transaction • Fatter the better. But not for FILE channel • Stable number ensures data consistency Footer
- 29. Apache Flume - FYI Design considerations - II • Channel capacity and transaction capacity • For MEMORY channel, channel capacity is limited by RAM size. • For FILE, channel capacity is limited by disk size • Should not exceed batch size configured for the sinks • Channel selector • An event can either be replicated or multiplexed • Preferable vs conditional • Handle high throughput systems • Tiered architecture to handle event flow • Aggregate and push approach Footer