2022 Blog Data Process Systems 
Big Data Tools
MAY 19, 2022
Flink 1.15.0 – What I like about this release of Flink, a top framework for streaming data processing, is that it comes with quality documentation. Some systems think that it should be in milliseconds, and some think that it should be in seconds. That wraps up April’s Data Engineering Annotated.
Big Data Tools
MAY 19, 2022
Flink 1.15.0 – What I like about this release of Flink, a top framework for streaming data processing, is that it comes with quality documentation. Some systems think that it should be in milliseconds, and some think that it should be in seconds. That wraps up April’s Data Engineering Annotated.
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Data Engineering Weekly
DECEMBER 21, 2022
The Data world Before Hadoop Era We must walk through memory lane to understand why functional data engineering is critical. Let’s reference what the data world looked like before the Hadoop era. Any blog is incomplete if it does not include a Gartner prediction, so let’s start with one. Why is it so?
Snowflake
SEPTEMBER 11, 2023
Quebec takes that a step further with its Bill 64, now referred to as Law 25, which modernizes data protection and privacy legislation for Canada’s second most populated province. Law 25 takes a phased approach to its requirements, with the first group of requirements going into effect on September 22, 2022. 1, Section 3.2,
DataKitchen
MARCH 12, 2024
Thanks to Observability, I could diagnose the problem – definitely helped me a lot during the process.” Data errors can cause compliance risks. Business users lose trust in the data and have an opportunity cost. Find and Respond to Problems Faster Key Benefit Problems in complex data systems will happen.
Cloudera
MARCH 3, 2022
Inconsistent data access policies may also mean a data practitioner is making decisions on incomplete or out-of-date information. . Data quality and lineage issues also lead to inconsistent insights and, with that, decisions that impact the business’ ability to innovate and differentiate.
Cloudera
JUNE 18, 2022
In order to realize the benefits of both worlds — flexibility of analytics in data lakes, and simple and fast SQL in data warehouses — companies often deployed data lakes to complement their data warehouses, with the data lake feeding a data warehouse system as the last step of an extract, transform, load (ETL) or ELT pipeline.
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