Remove Big Data Remove Data Processing Remove Data Warehouse
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Top 4 Cloud Platforms to Host or Run Docker Containers for Free

Analytics Vidhya

Containerizing is all about bundling up a software application/service and isolating it from the host environment […] The post Top 4 Cloud Platforms to Host or Run Docker Containers for Free appeared first on Analytics Vidhya.

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The DataOps Vendor Landscape, 2021

DataKitchen

Piperr.io — Pre-built data pipelines across enterprise stakeholders, from IT to analytics, tech, data science and LoBs. Prefect Technologies — Open-source data engineering platform that builds, tests, and runs data workflows. Genie — Distributed big data orchestration service by Netflix.

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Unlocking near real-time analytics with petabytes of transaction data using Amazon Aurora Zero-ETL integration with Amazon Redshift and dbt Cloud

AWS Big Data

While customers can perform some basic analysis within their operational or transactional databases, many still need to build custom data pipelines that use batch or streaming jobs to extract, transform, and load (ETL) data into their data warehouse for more comprehensive analysis.

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Amazon Q data integration adds DataFrame support and in-prompt context-aware job creation

AWS Big Data

You can now generate data integration jobs for various data sources and destinations, including Amazon Simple Storage Service (Amazon S3) data lakes with popular file formats like CSV, JSON, and Parquet, as well as modern table formats such as Apache Hudi , Delta , and Apache Iceberg.

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The future of data: A 5-pillar approach to modern data management

CIO Business Intelligence

Manish Limaye Pillar #1: Data platform The data platform pillar comprises tools, frameworks and processing and hosting technologies that enable an organization to process large volumes of data, both in batch and streaming modes. The choice of vendors should align with the broader cloud or on-premises strategy.

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How Will The Cloud Impact Data Warehousing Technologies?

Smart Data Collective

Dating back to the 1970s, the data warehousing market emerged when computer scientist Bill Inmon first coined the term ‘data warehouse’. Created as on-premise servers, the early data warehouses were built to perform on just a gigabyte scale. Big data and data warehousing.

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Introduction To The Basic Business Intelligence Concepts

datapine

“Without big data, you are blind and deaf and in the middle of a freeway.” – Geoffrey Moore, management consultant, and author. In a world dominated by data, it’s more important than ever for businesses to understand how to extract every drop of value from the raft of digital insights available at their fingertips.