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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 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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A Complete Guide to Azure Data Engineer Certification (DP-203)

Knowledge Hut

Human society in 2023 is a digital world, and its fuel - its currency - is data. Today, organizations seek skilled professionals who can harness data’s power to drive informed decisions. As technology evolves, cloud platforms have emerged as the cornerstone of modern data management.

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New Fivetran connector streamlines data workflows for real-time insights

ThoughtSpot

The pathway from ETL to actionable analytics can often feel disconnected and cumbersome, leading to frustration for data teams and long wait times for business users. And even when we manage to streamline the data workflow, those insights aren’t always accessible to users unfamiliar with antiquated business intelligence tools.

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Data Orchestration: Defining, Understanding, and Applying

Ascend.io

When data is infrequently updated or accessed, it’s possible to utilize raw data directly to fulfill business objectives, provided that cost and performance metrics are met. However, this approach quickly shows its limitations as data volume escalates. So, why is data orchestration a big deal?

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Build vs Buy Data Pipeline Guide

Monte Carlo

In an evolving data landscape, the explosion of new tooling solutions—from cloud-based transforms to data observability —has made the question of “build versus buy” increasingly important for data leaders. There are two primary types of raw data. The scale of data events depends entirely on the product.

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Data Pipeline Architecture Explained: 6 Diagrams and Best Practices

Monte Carlo

Some organizations choose to still leverage an ETL pattern in the cloud, particularly for production pipelines where data contracts can help reduce data downtime. The term modern data stack refers to the multiple modular SaaS solutions that comprise the data platform and pipeline (more on those later).