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CI/CD for Data Pipelines: A Game-Changer with AnalyticsCreator

Data Science Blog

Continuous Integration and Continuous Delivery (CI/CD) for Data Pipelines: It is a Game-Changer with AnalyticsCreator! The need for efficient and reliable data pipelines is paramount in data science and data engineering. They transform data into a consistent format for users to consume.

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Testing and Monitoring Data Pipelines: Part Two

Dataversity

In part one of this article, we discussed how data testing can specifically test a data object (e.g., table, column, metadata) at one particular point in the data pipeline.

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Becoming a Data Engineer: 7 Tips to Take Your Career to the Next Level

Data Science Connect

Data engineering is a crucial field that plays a vital role in the data pipeline of any organization. It is the process of collecting, storing, managing, and analyzing large amounts of data, and data engineers are responsible for designing and implementing the systems and infrastructure that make this possible.

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Architect a mature generative AI foundation on AWS

Flipboard

A generative AI foundation can provide primitives such as models, vector databases, and guardrails as a service and higher-level services for defining AI workflows, agents and multi-agents, tools, and also a catalog to encourage reuse. Data quality is ownership of the consuming applications or data producers.

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Best Data Engineering Tools Every Engineer Should Know

Pickl AI

Summary: Data engineering tools streamline data collection, storage, and processing. Learning these tools is crucial for building scalable data pipelines. offers Data Science courses covering these tools with a job guarantee for career growth. What Does a Data Engineer Do?

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Demystifying Time Series Database: A Comprehensive Guide

Pickl AI

Summary: Time series databases (TSDBs) are built for efficiently storing and analyzing data that changes over time. This data, often from sensors or IoT devices, is typically collected at regular intervals. Buckle up as we navigate the intricacies of storing and analysing this dynamic data.

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Here’s Why Automation For Data Lakes Could Be Important

Smart Data Collective

Based on Microsoft’s discussion of the topic, CDC makes it much easier for a data store to accept changes within a database as it only updates the changed records of the database instead of reloading the entire tables that were affected. The Second Problem – Quickly Querying Data.