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Data Engineering Zoomcamp – Data Ingestion (Week 2)

Hepta Analytics

DE Zoomcamp 2.2.1 – Introduction to Workflow Orchestration Following last weeks blog , we move to data ingestion. We already had a script that downloaded a csv file, processed the data and pushed the data to postgres database. This week, we got to think about our data ingestion design.

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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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Build vs Buy Data Pipeline Guide

Monte Carlo

In this article, we’ll dive deep into the data presentation layers of the data stack to consider how scale impacts our build versus buy decisions, and how we can thoughtfully apply our five considerations at various points in our platform’s maturity to find the right mix of components for our organizations unique business needs.

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Data Pipeline Architecture Explained: 6 Diagrams and Best Practices

Monte Carlo

Why is data pipeline architecture important? Databricks – Databricks, the Apache Spark-as-a-service platform, has pioneered the data lakehouse, giving users the options to leverage both structured and unstructured data and offers the low-cost storage features of a data lake.

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Snowflake Releases New Geospatial Innovations, Now with CARTO Workflows Integration

Snowflake

It seems everyone has a handful of such shapes in their raw data, and in the past they had to fix those shapes outside of Snowflake before ingesting them. Introducing CARTO Workflows Snowflake’s powerful data ingestion and transformation features help many data engineers and analysts who prefer SQL.

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

AltexSoft

Data storage The tools mentioned in the previous section are instrumental in moving data to a centralized location for storage, usually, a cloud data warehouse, although data lakes are also a popular option. But this distinction has been blurred with the era of cloud data warehouses.

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