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Sqoop vs. Flume Battle of the Hadoop ETL tools

ProjectPro

Some of the common challenges with data ingestion in Hadoop are parallel processing, data quality, machine data on a higher scale of several gigabytes per minute, multiple source ingestion, real-time ingestion and scalability. Need for Apache Sqoop How Apache Sqoop works? Need for Flume How Apache Flume works?

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Complete Guide to Data Transformation: Basics to Advanced

Ascend.io

Intermediate Data Transformation Techniques Data engineers often find themselves in the thick of transforming data into formats that are not only usable but also insightful. Intermediate data transformation techniques are where the magic truly begins.

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Azure Data Factory vs AWS Glue-The Cloud ETL Battle

ProjectPro

A survey by Data Warehousing Institute TDWI found that AWS Glue and Azure Data Factory are the most popular cloud ETL tools with 69% and 67% of the survey respondents mentioning that they have been using them. Both platforms are designed for data transformation and preparation.

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The Good and the Bad of Apache Kafka Streaming Platform

AltexSoft

The technology was written in Java and Scala in LinkedIn to solve the internal problem of managing continuous data flows. This enables systems using Kafka to aggregate data from many sources and to make it consistent. Instead of interfering with each other, Kafka consumers create groups and split data among themselves.

Kafka 93
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Data Marts: What They Are and Why Businesses Need Them

AltexSoft

The step involving data transfer, filtering, and loading into either a data warehouse or data mart is called the extract-transform-load (ELT) process. When dealing with dependent data marts, the central data warehouse already keeps data formatted and cleansed, so ETL tools will do little job.

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Tips to Build a Robust Data Lake Infrastructure

DareData

The architecture of a data lake project may contain multiple components, including the Data Lake itself, one or multiple Data Warehouses or one or multiple Data Marts. The Data Lake acts as the central repository for aggregating data from diverse sources in its raw format.

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Data Warehousing Guide: Fundamentals & Key Concepts

Monte Carlo

A company’s production data, third-party ads data, click stream data, CRM data, and other data are hosted on various systems. An ETL tool or API-based batch processing/streaming is used to pump all of this data into a data warehouse. The following diagram explains how integrations work.