Remove Data Process Remove Events Remove Metadata Remove Relational Database
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97 things every data engineer should know

Grouparoo

This provided a nice overview of the breadth of topics that are relevant to data engineering including data warehouses/lakes, pipelines, metadata, security, compliance, quality, and working with other teams. For example, grouping the ones about metadata, discoverability, and column naming might have made a lot of sense.

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Data Lake Explained: A Comprehensive Guide to Its Architecture and Use Cases

AltexSoft

Instead of relying on traditional hierarchical structures and predefined schemas, as in the case of data warehouses, a data lake utilizes a flat architecture. This structure is made efficient by data engineering practices that include object storage. Watch our video explaining how data engineering works.

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Accenture’s Smart Data Transition Toolkit Now Available for Cloudera Data Platform

Cloudera

Running on CDW is fully integrated with streaming, data engineering, and machine learning analytics. It has a consistent framework that secures and provides governance for all data and metadata on private clouds, multiple public clouds, or hybrid clouds. Smart DwH Mover helps in accelerating data warehouse migration.

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AWS Glue-Unleashing the Power of Serverless ETL Effortlessly

ProjectPro

AWS Glue is a widely-used serverless data integration service that uses automated extract, transform, and load ( ETL ) methods to prepare data for analysis. It offers a simple and efficient solution for data processing in organizations. Then, Glue writes the job's metadata into the embedded AWS Glue Data Catalog.

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Hadoop vs Spark: Main Big Data Tools Explained

AltexSoft

Hadoop and Spark are the two most popular platforms for Big Data processing. They both enable you to deal with huge collections of data no matter its format — from Excel tables to user feedback on websites to images and video files. Obviously, Big Data processing involves hundreds of computing units.

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ELT Process: Key Components, Benefits, and Tools to Build ELT Pipelines

AltexSoft

ELT makes it easier to manage and access all this information by allowing both raw and cleaned data to be loaded and stored for further analysis. With the ETL shift from a traditional on-premise variant to a cloud solution, you can also use it to work with different data sources and move a lot of data. Full extraction.

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20 Best Open Source Big Data Projects to Contribute on GitHub

ProjectPro

It serves as a distributed processing engine for both categories of data streams: unbounded and bounded. Support for stream and batch processing, comprehensive state management, event-time processing semantics, and consistency guarantee for the state are just a few of Flink's capabilities.