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 (..)
Data Engineering Podcast
JANUARY 7, 2022
Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. What are the core requirements for building a reverse ETL system? What are the additional capabilities that users of the system ask for as they get more advanced in their usage?
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Ascend.io
DECEMBER 21, 2022
For example, Salesforce, Hubspot, or Marketo To get the data into these operational systems, data teams used to write their own API connectors from the data warehouse to SaaS solutions. Unfortunately, APIs are not designed to support real-time data transfer. Reverse ETL emerged as a result of these difficulties.
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.
Knowledge Hut
MAY 2, 2024
Let’s see a comparison between Spark and MapReduce on different other parameters to understand where to use Spark and where to use MapReduce Attributes MapReduce Apache Spark Speed/Performance MapReduce is designed for batch processing and is not as fast as Spark. Spark can also handle Streaming data so it's best suited for Lambda design.
Striim
AUGUST 31, 2023
In this article, we will take a look at the benefits and drawbacks of kappa architecture, how Striim makes it easier to use, what infrastructure you need for your kappa architecture, and how you can start designing your own kappa architecture with a free version of Striim’s unified data integration and streaming platform.
Precisely
APRIL 3, 2023
Most traditional infrastructures were designed in an era when batch processing was the norm. Those systems are ill-suited to keep pace with businesses that need to ingest and analyze data in real time. In today’s world, data comes from diverse sources, in different types and formats, and at varying speeds.
Expert insights. Personalized for you.
130 
Let's personalize your content