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8 Data Ingestion Tools (Quick Reference Guide)

Monte Carlo

At the heart of every data-driven decision is a deceptively simple question: How do you get the right data to the right place at the right time? The growing field of data ingestion tools offers a range of answers, each with implications to ponder. Fivetran Image courtesy of Fivetran.

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Data Ingestion: 7 Challenges and 4 Best Practices

Monte Carlo

Data ingestion is the process of collecting data from various sources and moving it to your data warehouse or lake for processing and analysis. It is the first step in modern data management workflows. Table of Contents What is Data Ingestion? Decision making would be slower and less accurate.

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Complete Guide to Data Ingestion: Types, Process, and Best Practices

Databand.ai

Complete Guide to Data Ingestion: Types, Process, and Best Practices Helen Soloveichik July 19, 2023 What Is Data Ingestion? Data Ingestion is the process of obtaining, importing, and processing data for later use or storage in a database. In this article: Why Is Data Ingestion Important?

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The Ultimate Fivetran Alternative: A Football-Inspired Approach to Data Management

Ascend.io

This same principle holds true in data management. You require a comprehensive solution that addresses every facet, from ingestion and transformation to orchestration and reverse ETL. Defense: Saving Money with Intelligent Data Refresh In football, a solid defense does more than just stop goals.

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Strategies And Tactics For A Successful Master Data Management Implementation

Data Engineering Podcast

Summary The most complicated part of data engineering is the effort involved in making the raw data fit into the narrative of the business. Master Data Management (MDM) is the process of building consensus around what the information actually means in the context of the business and then shaping the data to match those semantics.

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Real-Time Data Ingestion: Snowflake, Snowpipe and Rockset

Rockset

With Snowflake, organizations get the simplicity of data management with the power of scaled-out data and distributed processing. Although Snowflake is great at querying massive amounts of data, the database still needs to ingest this data. Data ingestion must be performant to handle large amounts of data.

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Self Service Data Management From Ingest To Insights With Isima

Data Engineering Podcast

At Isima they decided to reimagine the entire ecosystem from the ground up and built a single unified platform to allow end-to-end self service workflows from data ingestion through to analysis. What was your motivation for creating a new platform for data applications? What is the story behind the name?