AWS announces Amazon RDS for MySQL zero-ETL integration with Amazon Redshift (Public Preview)
Amazon Relational Database Service (Amazon RDS) for MySQL zero-ETL integration with Amazon Redshift allows you to access transactional data from Amazon RDS for MySQL to run analytics and machine learning (ML) on petabytes of data in Amazon Redshift. With the zero-ETL integration, you don’t need to build and maintain complex data pipelines to perform extract, transform, and load (ETL) operations. T
Amazon Aurora MySQL zero-ETL integration with Amazon Redshift is now generally available | Amazon Web Services
AWS News Blog Amazon Aurora MySQL zero-ETL integration with Amazon Redshift is now generally available “Data is at the center of every application, process, and business decision,†wrote Swami Sivasubramanian, VP of Database, Analytics, and Machine Learning at AWS, and I couldn’t agree more. A common pattern customers use today is to build data pipelines to move data from Amazon Aurora to Amazon R
AWS Glue visual ETL now supports new native Amazon Redshift capabilities
AWS Glue Studio now supports new native Amazon Redshift connector capabilities: browse Amazon Redshift tables directly in Glue Studio, add native Redshift SQL, execute common operations while writing to Amazon Redshift including drop, truncate, upsert, create or merge. AWS Glue Studio offers a visual extract-transform-and-load (ETL) interface that helps ETL developers to author, run, and monitor A
Amazon Redshift now supports auto-copy from Amazon S3
Amazon Redshift launches the preview of auto-copy support to simplify data loading from Amazon S3 into Amazon Redshift. You can now setup continuous file ingestion rules to track your Amazon S3 paths and automatically load new files without the need for additional tools or custom solutions. Amazon Redshift customers run COPY statements to load data into their local tables from various data sources
AWS announces Amazon Aurora zero-ETL integration with Amazon Redshift
Amazon Aurora now supports zero-ETL integration with Amazon Redshift, to enable near real-time analytics and machine learning (ML) using Amazon Redshift on petabytes of transactional data from Aurora. Within seconds of transactional data being written into Aurora, the data is available in Amazon Redshift, so you don’t have to build and maintain complex data pipelines to perform extract, transform,
Extract, load, and transform your data with PlanetScale Connect — PlanetScale
There are many reasons you may need to move and/or transform your application data: to improve database performance, consolidate data, provide access to other teams in your organization to safely query data, and other reasons specific to your use case. This may seem straightforward to set up yourself, but this can be tedious and difficult to get right without impacting your production database. Wi
Visual Data Preparation – AWS Glue DataBrew – AWS
AWS Glue DataBrew is a visual data preparation tool that makes it easier for data analysts and data scientists to clean and normalize data to prepare it for analytics and machine learning (ML). You can choose from over 250 prebuilt transformations to automate data preparation tasks, all without the need to write any code. You can automate filtering anomalies, converting data to standard formats an
DBT: Data Build Tool with Tristan Handy - Software Engineering Daily
A data warehouse serves the purpose of providing low latency queries for high volumes of data. A data warehouse is often part of a data pipeline, which moves data through different areas of infrastructure in order to build applications such as machine learning models, dashboards, and reports. Modern data pipelines are often associated with the term “ELT†or Extract, Load, Transform. In the “ELT†w
ETL and ELT design patterns for lake house architecture using Amazon Redshift: Part 1 | Amazon Web Services
AWS Big Data Blog ETL and ELT design patterns for lake house architecture using Amazon Redshift: Part 1 New: Read Amazon Redshift continues its price-performance leadership to learn what analytic workload trends we’re seeing from Amazon Redshift customers, new capabilities we have launched to improve Redshift’s price-performance, and the results from the latest benchmarks. Part 1 of this multi-pos
pgloader - migrate MySQL to PostgresQL
pgloader loads data into PostgreSQL and allows you to implement Continuous Migration from your current database to PostgreSQL. Read the White Paper to learn how to limit risks and control your budget, and start your PostgreSQL migration today! Introduction pgloader has two modes of operation. It can either load data from files, such as CSV or Fixed-File Format; or migrate a whole database to Postg
dbt Labs | Transform Data in Your Warehouse
Less data work. More data that worksWhen your data is organized, you can deliver data products people trust. Transform data faster, cheaper, and more reliably with dbt. Deliver data that business can trust — and traceReliable data increases trust and fuels wide-reaching business decisions. This saves time and resources and improves brand equity.
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AWS Glue for Apache Spark announces native connectivity for Google BigQuery
AWS Glue Streaming ETL Auto Scaling is now generally available
GitHub - PRQL/prql: PRQL is a modern language for transforming data — a simple, powerful, pipelined SQL replacement
Engineers Shouldn’t Write ETL: A Guide to Building a High Functioning Data Science Department | Stitch Fix Technology – Multithreaded
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