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Eight Top DataOps Trends for 2022

DataKitchen

Keep an eye on the eight top trends below that we believe will be significant in the year 2022. The data industry realizes that AI bias is simply a quality problem, and AI systems should be subject to this same level of process control as an automobile rolling off an assembly line. Data Gets Meshier. AI Accountability.

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Use Apache Iceberg in a data lake to support incremental data processing

AWS Big Data

Iceberg has become very popular for its support for ACID transactions in data lakes and features like schema and partition evolution, time travel, and rollback. In early 2022, AWS announced general availability of Athena ACID transactions, powered by Apache Iceberg. and later supports the Apache Iceberg framework for data lakes.

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Simplify data ingestion from Amazon S3 to Amazon Redshift using auto-copy

AWS Big Data

The following are the recommended best practices when working with files using the auto-copy job: Use unique file names for each file in a auto-copy job (for example, 2022-10-15-batch-1.csv Do not overwrite existing files. He was the CEO and co-founder of DataRow, which was acquired by Amazon in 2020.

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2021 Gift Giving Guide for Data Nerds

DataKitchen

Data Mesh: Delivering Data-Driven Value at Scale , by Zhamak Dehghani. This book is not available until January 2022, but considering all the hype around the data mesh, we expect it to be a best seller. with subject line ‘Data Nerd Gift Ideas’ and we’d be happy to put them in a follow-up blog post.

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Enable business users to analyze large datasets in your data lake with Amazon QuickSight

AWS Big Data

This blog post is co-written with Ori Nakar from Imperva. Events and many other security data types are stored in Imperva’s Threat Research Multi-Region data lake. Imperva harnesses data to improve their business outcomes. Imperva’s data lake has a few dozen different datasets, in the scale of petabytes.

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Implement tag-based access control for your data lake and Amazon Redshift data sharing with AWS Lake Formation

AWS Big Data

Data-driven organizations treat data as an asset and use it across different lines of business (LOBs) to drive timely insights and better business decisions. This leads to having data across many instances of data warehouses and data lakes using a modern data architecture in separate AWS accounts.

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Build a transactional data lake using Apache Iceberg, AWS Glue, and cross-account data shares using AWS Lake Formation and Amazon Athena

AWS Big Data

Building a data lake on Amazon Simple Storage Service (Amazon S3) provides numerous benefits for an organization. However, many use cases, like performing change data capture (CDC) from an upstream relational database to an Amazon S3-based data lake, require handling data at a record level.