Start Data Engineering
JULY 11, 2020
Intro A very common use case in data engineering is to build a ETL system for a data warehouse, to have data loaded in from multiple separate databases to enable data analysts/scientists to be able to run queries on this data, since the source databases are used by your applications and we do not want these analytic queries to affect our application (..)
Grouparoo
OCTOBER 26, 2021
This includes the different possible sources of data such as application APIs, social media, relational databases, IoT device sensors, and data lakes. This may include a data warehouse when it’s necessary to pipeline data from your warehouse to various destinations as in the case of a reverse ETL pipeline. featured image via unsplash
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Grouparoo
FEBRUARY 9, 2022
ETL testing can be challenging since most ETL systems process large volumes of heterogeneous data. However, establishing clear requirements from the start can make it easier for ETL testers to perform the required tests. Stages of the ETL Testing Process The ETL testing process can be broken down into 8 different stages.
Ascend.io
DECEMBER 21, 2022
How to Fit Reverse ETL Into Your Data Architecture Once businesses comprehend the advantages of reverse ETL, the question often is whether you should buy a reverse ETL solution or use your data team to build one for your company. First, building your custom reverse ETL system is more expensive than you think.
Precisely
APRIL 3, 2023
The Long Road from Batch to Real-Time Traditional “extract, transform, load” (ETL) systems were built under certain constraints, stemming from the cost of technology and implementation resources, as well as the inherent limits of computational power. Today’s world calls for a streaming-first approach.
Striim
AUGUST 31, 2023
Two different systems are required for creating a kappa architecture: one for streaming data and another for batch processing. Stream processors, storage layers, message brokers, and databases make up the basic components of this architecture.
Knowledge Hut
MAY 2, 2024
To store and process even only a fraction of this amount of data, we need Big Data frameworks as traditional Databases would not be able to store so much data nor traditional processing systems would be able to process this data quickly. By 2020, it’s estimated that 1.7MB of data will be created every second for every person on earth.
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