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Data Engineering Zoomcamp – Data Ingestion (Week 2)

Hepta Analytics

This week, we got to think about our data ingestion design. We looked at the following: How do we ingest – ETL vs ELT Where do we store the dataData lake vs data warehouse Which tool to we use to ingest – cronjob vs workflow engine NOTE : This weeks task requires good internet speed and good compute.

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DataOps Architecture: 5 Key Components and How to Get Started

Databand.ai

DataOps is a collaborative approach to data management that combines the agility of DevOps with the power of data analytics. It aims to streamline data ingestion, processing, and analytics by automating and integrating various data workflows.

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Data Orchestration: Defining, Understanding, and Applying

Ascend.io

Data orchestration is the process of efficiently coordinating the movement and processing of data across multiple, disparate systems and services within a company. However, this approach quickly shows its limitations as data volume escalates. So, why is data orchestration a big deal?

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Data Engineering Weekly #114

Data Engineering Weekly

SiliconANGLE theCUBE: Analyst Predictions 2023 - The Future of Data Management By far one of the best analyses of trends in Data Management. 2023 predictions from the panel are; Unified metadata becomes kingmaker. The names hold less meaning to the outcome, but its fancy. link] All rights reserved ProtoGrowth Inc, India.

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Data Pipeline Architecture Explained: 6 Diagrams and Best Practices

Monte Carlo

The modern data stack era , roughly 2017 to present data, saw the widespread adoption of cloud computing and modern data repositories that decoupled storage from compute such as data warehouses, data lakes, and data lakehouses. It is like a smart scheduler for your data workflows.

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

AltexSoft

Built around a cloud data warehouse, data lake, or data lakehouse. Modern data stack tools are designed to integrate seamlessly with cloud data warehouses such as Redshift, Bigquery, and Snowflake, as well as data lakes or even the child of the first two — a data lakehouse.

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