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Bulldozer: Batch Data Moving from Data Warehouse to Online Key-Value Stores

Netflix Tech

Usually Data scientists and engineers write Extract-Transform-Load (ETL) jobs and pipelines using big data compute technologies, like Spark or Presto , to process this data and periodically compute key information for a member or a video. The processed data is typically stored as data warehouse tables in AWS S3.

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The Evolution of Table Formats

Monte Carlo

At its core, a table format is a sophisticated metadata layer that defines, organizes, and interprets multiple underlying data files. Table formats incorporate aspects like columns, rows, data types, and relationships, but can also include information about the structure of the data itself.

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Data Catalog - A Broken Promise

Data Engineering Weekly

Data catalogs are the most expensive data integration systems you never intended to build. Data Catalog as a passive web portal to display metadata requires significant rethinking to adopt modern data workflow, not just adding “modern” in its prefix. The modern(?)

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Unleashing the Power of CDC With Snowflake

Workfall

It ensures that organisations stay at the forefront by capturing every twist and turn in the data landscape. With CDC by their side, organisations unlock the power of informed decision-making, safeguard data integrity, and enable lightning-fast analytics. CDC also plays a crucial role in data integration and ETL processes.

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DataOps Tools: Key Capabilities & 5 Tools You Must Know About

Databand.ai

By using DataOps tools, organizations can break down silos, reduce time-to-insight, and improve the overall quality of their data analytics processes. DataOps tools can be categorized into several types, including data integration tools, data quality tools, data catalog tools, data orchestration tools, and data monitoring tools.

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The Modern Data Stack: What It Is, How It Works, Use Cases, and Ways to Implement

AltexSoft

As the volume and complexity of data continue to grow, organizations seek faster, more efficient, and cost-effective ways to manage and analyze data. In recent years, cloud-based data warehouses have revolutionized data processing with their advanced massively parallel processing (MPP) capabilities and SQL support.

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The Good and the Bad of Apache Airflow Pipeline Orchestration

AltexSoft

DevOps tasks — for example, creating scheduled backups and restoring data from them. Airflow is especially useful for orchestrating Big Data workflows. Airflow is not a data processing tool by itself but rather an instrument to manage multiple components of data processing. Metadata database.