ThoughtSpot
SEPTEMBER 6, 2023
Those coveted insights live at the end of a process lovingly known as the data pipeline. 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. Keep reading to see how it works.
Monte Carlo
APRIL 24, 2023
Data ingestion When we think about the flow of data in a pipeline, data ingestion is where the data first enters our platform. There are two primary types of raw data. And data orchestration tools are generally easy to stand-up for initial use-cases. Missed Nishith’s 5 considerations?
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Monte Carlo
JUNE 14, 2023
In this post, we will help you quickly level up your overall knowledge of data pipeline architecture by reviewing: Table of Contents What is data pipeline architecture? Why is data pipeline architecture important? What is data pipeline architecture? Why is data pipeline architecture important?
DataKitchen
MAY 10, 2024
Data Migration : This use case focuses on verifying data accuracy during migration projects, such as cloud transitions, to ensure that migrated data matches the legacy data regarding output and functionality. Are all required data records and values present and accurate?
Ascend.io
DECEMBER 11, 2023
Here’s the deal: for data to truly drive your business forward, you need a reliable and scalable system to keep it moving without hiccups. In other words, you need data orchestration. In this article, we’ll break down what data orchestration is, its significance, and how it differs from data pipeline orchestration.
Databand.ai
AUGUST 30, 2023
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.
Meltano
OCTOBER 5, 2022
In the same way, a DataOps engineer designs the data assembly line that enables data scientists to derive insights from data analytics faster and with fewer errors. DataOps engineers improve the speed and quality of the data development process by applying DevOps principles to data workflow, known as DataOps.
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