Selecting a Data Lake ETL Platform? Here Are 6 Questions to Ask
Not all data lakes are created equal. If your organization wants to adopt a data lake solution to simplify and more easily operate your IT infrastructure and store enormous quantities of data without requiring extended data transformation, then go for it.
But before you do, understand that simply dumping all your data into object storage such as AWS S3 doesn’t exactly mean you will have a working data lake.
The ability to use that data in analytics or machine learning requires converting that raw information into organized datasets you can use for SQL queries, and this can only be done via extract-transform-load (ETL) flows.
Data lake ETL platforms are available in a full range of options – from open-source to managed solutions to custom-built. Whichever tool you select, it’s important to differentiate data lake ETL challenges from traditional database ELT demands – and seek the platform that overcomes these obstacles.
Ask yourself which ETL solution:
1. Effectively Conducts Stateful Transformations
Traditional ETL frameworks allow for stateful operations like joins and aggregations to enable analysts to work with data from multiple sources; this is difficult to implement with a decoupled architecture.
Stateful transformations can occur by relying on extract-load-transform (ELT) – i.e., sending data to an “intermediary” database and using the database’s SQL, processing power and already amassed historical data. After transformation, the information is loaded into the data warehouse tables.
Data lakes, aiming to reduce cost and complexity by avoiding decoupled architecture, cannot depend on databases for every activity. You’ll need to look for an ETL tool that can conduct stateful transformations in-memory and needs no additional database to sustain joins and aggregations.
2 Extracts Schema from Raw Data
Organizations customarily use data lakes as a storehouse for raw data in a structured or semi-structured arrangement vs databases, which are predicated on structured tables. This poses numerous challenges.
One, in order to query data, can the data lake ETL tool draw out a schema (without which querying is not possible) from the raw data – and bring it up to date as changes in data and data structure come about? And two — this is an ongoing struggle — can the ETL tool effectively make queries with nested data?
3 Improves Query Performance Via Optimized Object Storage
Have you tried to read raw data straight from a data lake? Unlike using a database’s optimized file system that quickly sends back query results, doing the same operation with a data lake can be quite frustrating performance-wise.
To get optimal results, your ETL framework should continually store data in columnar formats and merge small files to the 200mb-1gb range. Unlike traditional ELT tools that only need to write the data once to its target database, data lake ETL should support the ability to write multiple copies of the same data based on the queries you will want to run and the various optimizations required for your query engines to be performant.
4 Easily Integrates with the Metadata Catalog
You’ve chosen the data lake approach for its flexibility — store large quantities if data now but analyze it later — and the ability to handle a wide range of analytics use cases. Such an open architecture should keep metadata separate from the engine that queries it, so you can easily change these query engines or use several simultaneously for the same data.
This means the data lake tool you select should reinforce this open architecture, i.e., be seamlessly merged with the metadata catalog. This allows the metadata to be easily “queryable” by various services because it is both stored in the catalog and still dovetails with every adjustment in schema, partition, and location of objects.
5 Replays Historical Data
Say you wanted to test a hypothesis by looking at stored data on a historical basis. This is difficult to accomplish with the traditional database option, where data is stored in a mutable condition, and in which running such a query could be prohibitive in terms of cost, stress, and tension between operations and analysis.
It’s easy to do with a data lake. In data lakes, stored raw data remains continuously available – it only transformed after extraction. Therefore, having a data lake allows you got “travel back in time,” seeing the exact state of the data as it was collected.
“Traditional” databases don’t allow for that, as the data is only stored in its transformed state.
6 Updates Tables Periodically
Data lakes, unlike databases that allow you to update and make deletions to tables, contain partitioned files that enable an append or add-only feature. If you want to store transactional data, implement change data capture in the data lake, or delete particular data for GDPR compliance, you’ll have difficulty doing so.
Make sure that the data lake ETL tools you choose have the ability to sidestep this obstacle. Your solution should be able to allow upserts, a system that lets you insert new records or update existing ones, in the storage layer and in the output tables.
About the author: Ori Rafael is the CEO and co-founder of Upsolver, a provider of a self-service data lake ETL platform that bridges the gap between data lakes and data consumers. Ori has worked in IT for nearly two decades and has an MBA from Tel Aviv University.
Related Items:
Merging Batch and Stream Processing in a Post Lambda World
April 26, 2024
- Google Announces $75M AI Opportunity Fund and New Course to Skill One Million Americans
- Elastic Reports 8x Speed and 32x Efficiency Gains for Elasticsearch and Lucene Vector Database
- Gartner Identifies the Top Trends in Data and Analytics for 2024
- Satori and Collibra Accelerate AI Readiness Through Unified Data Management
- Argonne’s New AI Application Reduces Data Processing Time by 100x in X-ray Studies
April 25, 2024
- Salesforce Unveils Zero Copy Partner Network, Offering New Open Data Lake Access via Apache Iceberg
- Dataiku Enables Generative AI-Powered Chat Across the Enterprise
- IBM Transforms the Storage Ownership Experience with IBM Storage Assurance
- Cleanlab Launches New Solution to Detect AI Hallucinations in Language Models
- University of Maryland’s Smith School Launches New Center for AI in Business
- SAS Advances Public Health Research with New Analytics Tools on NIH Researcher Workbench
- NVIDIA to Acquire GPU Orchestration Software Provider Run:ai
April 24, 2024
- AtScale Introduces Developer Community Edition for Semantic Modeling
- Domopalooza 2024 Sets a High Bar for AI in Business Intelligence and Analytics
- BigID Highlights Crucial Security Measures for Generative AI in Latest Industry Report
- Moveworks Showcases the Power of Its Next-Gen Copilot at Moveworks.global 2024
- AtScale Announces Next-Gen Product Innovations to Foster Data-Driven Industry-Wide Collaboration
- New Snorkel Flow Release Empowers Enterprises to Harness Their Data for Custom AI Solutions
- Snowflake Launches Arctic: The Most Open, Enterprise-Grade Large Language Model
- Lenovo Advances Hybrid AI Innovation to Meet the Demands of the Most Compute Intensive Workloads
Most Read Features
Sorry. No data so far.
Most Read News In Brief
Sorry. No data so far.
Most Read This Just In
Sorry. No data so far.
Sponsored Partner Content
-
Get your Data AI Ready – Celebrate One Year of Deep Dish Data Virtual Series!
-
Supercharge Your Data Lake with Spark 3.3
-
Learn How to Build a Custom Chatbot Using a RAG Workflow in Minutes [Hands-on Demo]
-
Overcome ETL Bottlenecks with Metadata-driven Integration for the AI Era [Free Guide]
-
Gartner® Hype Cycle™ for Analytics and Business Intelligence 2023
-
The Art of Mastering Data Quality for AI and Analytics
Sponsored Whitepapers
Contributors
Featured Events
-
AI & Big Data Expo North America 2024
June 5 - June 6Santa Clara CA United States -
CDAO Canada Public Sector 2024
June 18 - June 19 -
AI Hardware & Edge AI Summit Europe
June 18 - June 19London United Kingdom -
AI Hardware & Edge AI Summit 2024
September 10 - September 12San Jose CA United States -
CDAO Government 2024
September 18 - September 19Washington DC United States