Grouparoo

OCTOBER 6, 2021

Tianhui Michael Li The Three Rs of Data Engineering by Tobias Macey Data testing and quality Automate Your Pipeline Tests by Tom White Data Quality for Data Engineers by Katharine Jarmul Data Validation Is More Than Summary Statistics by Emily Riederer The Six Words That Will Destroy Your Career by Bartosz Mikulski Your Data Tests Failed!

Data Engineering 52

Data Engineering Data Engineer Engineering Pipeline-centric 52

Data Engineering Weekly

OCTOBER 30, 2022

link] Dagster: Build a poor man’s data lake from scratch with DuckDB The value of the data is directly proportional to the recency of the data. The author narrates how the analytical requirement document can help to define a better data strategy. to solve some of the challenges with self-serving.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Ingestion 52

Grouparoo

DECEMBER 7, 2021

If undetected, corruption of data and its information will compromise the processes that utilize that data. Personal Data Collecting and managing data carries regulatory responsibilities regarding data protection and evidence required for regulatory compliance.

Data Integration 52

Data Integration Manufacturing ETL Tools Transportation 52

AltexSoft

NOVEMBER 23, 2021

If the transformation step comes after loading (for example, when data is consolidated in a data lake or a data lakehouse ), the process is known as ELT. You can learn more about how such data pipelines are built in our video about data engineering. Popular data virtualization tools.

Process 69

Process Data Lake Metadata Data Warehouse 69

Ascend.io

JULY 19, 2023

But something about data mesh feels different, doesn’t it? For one, data mesh tackles the real headaches caused by an overburdened data lake and the annoying game of tag that’s too often played between the people who make data, the ones who use it, and everyone else caught in the middle.

Data Governance 40

Data Governance Government Metadata Data 40

ProjectPro

NOVEMBER 30, 2021

The data sources can be an RDBMS or some file formats like XLSX, CSV, JSON, etc., We need to extract data from all the sources and convert it into a single format for standardized processing. Validate data: Validating the data after extraction is essential to ensure it matches the expected range and rejects it if it does not.

Process 52

Process Data Pipeline Data Warehouse AWS 52

ProjectPro

JANUARY 31, 2023

There are three steps involved in the deployment of a big data model: Data Ingestion: This is the first step in deploying a big data model - Data ingestion, i.e., extracting data from multiple data sources. It ensures that the data collected from cloud sources or local databases is complete and accurate.

Big Data 52

Big Data Hadoop AWS Relational Database 52