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Navigating the Chaos of Unruly Data: Solutions for Data Teams

DataKitchen

The core issue plaguing many organizations is the presence of out-of-control databases or data lakes characterized by: Unrestrained Data Changes: Numerous users and tools incessantly alter data, leading to a tumultuous environment. Monitor for freshness, schema changes, volume, field health/quality, new tables, and usage.

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Create a modern data platform using the Data Build Tool (dbt) in the AWS Cloud

AWS Big Data

In this post, we delve into a case study for a retail use case, exploring how the Data Build Tool (dbt) was used effectively within an AWS environment to build a high-performing, efficient, and modern data platform. It does this by helping teams handle the T in ETL (extract, transform, and load) processes. usr/local/airflow/.local/bin/dbt

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Orca Security’s journey to a petabyte-scale data lake with Apache Iceberg and AWS Analytics

AWS Big Data

With data becoming the driving force behind many industries today, having a modern data architecture is pivotal for organizations to be successful. In this post, we describe Orca’s journey building a transactional data lake using Amazon Simple Storage Service (Amazon S3), Apache Iceberg, and AWS Analytics.

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How Tricentis unlocks insights across the software development lifecycle at speed and scale using Amazon Redshift

AWS Big Data

It has been well published since the State of DevOps 2019 DORA Metrics were published that with DevOps, companies can deploy software 208 times more often and 106 times faster, recover from incidents 2,604 times faster, and release 7 times fewer defects. Finally, data integrity is of paramount importance.

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What is a Data Pipeline?

Jet Global

The key components of a data pipeline are typically: Data Sources : The origin of the data, such as a relational database , data warehouse, data lake , file, API, or other data store. This can include tasks such as data ingestion, cleansing, filtering, aggregation, or standardization.