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Top Data Lake Vendors (Quick Reference Guide)

Monte Carlo

Data lakes are useful, flexible data storage repositories that enable many types of data to be stored in its rawest state. Traditionally, after being stored in a data lake, raw data was then often moved to various destinations like a data warehouse for further processing, analysis, and consumption.

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Data Lake Explained: A Comprehensive Guide to Its Architecture and Use Cases

AltexSoft

In 2010, a transformative concept took root in the realm of data storage and analytics — a data lake. The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data. What is a data lake?

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New Fivetran connector streamlines data workflows for real-time insights

ThoughtSpot

And even when we manage to streamline the data workflow, those insights aren’t always accessible to users unfamiliar with antiquated business intelligence tools. That’s why ThoughtSpot and Fivetran are joining forces to decrease the amount of time, steps, and effort required to go from raw data to AI-powered insights.

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How to Design a Modern, Robust Data Ingestion Architecture

Monte Carlo

Data Transformation : Clean, format, and convert extracted data to ensure consistency and usability for both batch and real-time processing. Data Loading : Load transformed data into the target system, such as a data warehouse or data lake.

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How to Build a Data Pipeline in 6 Steps

Ascend.io

The key differentiation lies in the transformational steps that a data pipeline includes to make data business-ready. Ultimately, the core function of a pipeline is to take raw data and turn it into valuable, accessible insights that drive business growth. best suit our processed data? cleaning, formatting)?

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Data Lakehouse: Concept, Key Features, and Architecture Layers

AltexSoft

The pun being obvious, there’s more to that than just a new term: Data lakehouses combine the best features of both data lakes and data warehouses and this post will explain this all. What is a data lakehouse? Data warehouse vs data lake vs data lakehouse: What’s the difference.

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Addressing Data Mesh Technical Challenges with DataOps

DataKitchen

The data industry has a wide variety of approaches and philosophies for managing data: Inman data factory, Kimball methodology, s tar schema , or the data vault pattern, which can be a great way to store and organize raw data, and more. Data mesh does not replace or require any of these.