Remove 2022 Remove Data Warehouse Remove Optimization Remove Snapshot
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How the Edge Is Changing Data-First Modernization

CIO Business Intelligence

The advent of distributed workforces, smart devices, and internet-of-things (IoT) applications is creating a deluge of data generated and consumed outside of traditional centralized data warehouses. billion connected IoT devices by 2025, generating almost 80 billion zettabytes of data at the edge. over last year.

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

AWS Big Data

In early 2022, AWS announced general availability of Athena ACID transactions, powered by Apache Iceberg. Whenever there is an update to the Iceberg table, a new snapshot of the table is created, and the metadata pointer points to the current table metadata file. The snapshot points to the manifest list.

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Open Data Lakehouse powered by Iceberg for all your Data Warehouse needs

Cloudera

In this blog, we will share with you in detail how Cloudera integrates core compute engines including Apache Hive and Apache Impala in Cloudera Data Warehouse with Iceberg. We will publish follow up blogs for other data services. Iceberg basics Iceberg is an open table format designed for large analytic workloads.

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Getting started guide for near-real time operational analytics using Amazon Aurora zero-ETL integration with Amazon Redshift

AWS Big Data

Amazon Aurora zero-ETL integration with Amazon Redshift was announced at AWS re:Invent 2022 and is now available in public preview for Amazon Aurora MySQL-Compatible Edition 3 (compatible with MySQL 8.0) For this illustration, we use a provisioned Aurora database and an Amazon Redshift Serverless data warehouse. or higher).

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Perform upserts in a data lake using Amazon Athena and Apache Iceberg

AWS Big Data

It supports modern analytical data lake operations such as create table as select (CTAS), upsert and merge, and time travel queries. Athena also supports the ability to create views and perform VACUUM (snapshot expiration) on Apache Iceberg tables to optimize storage and performance. Create a table to point to the CDC data.

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Join a streaming data source with CDC data for real-time serverless data analytics using AWS Glue, AWS DMS, and Amazon DynamoDB

AWS Big Data

For Task logs , enable Turn on CloudWatch logs and Turn on batch-optimized apply. Create a Python file called generate-data-for-kds.py : $ python3 generate-data-for-kds.py client("kinesis")) This script puts a Kinesis data stream record every 2 seconds. For Stop task after full load completes , choose Don’t stop.

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Choosing an open table format for your transactional data lake on AWS

AWS Big Data

A modern data architecture enables companies to ingest virtually any type of data through automated pipelines into a data lake, which provides highly durable and cost-effective object storage at petabyte or exabyte scale.

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