Data Engineering Weekly

FEBRUARY 18, 2025

Fluss is a compelling new project in the realm of real-time data processing. I spoke with Jark Wu , who leads the Fluss and Flink SQL team at Alibaba Cloud, to understand its origins and potential. Kafka is designed for streaming events, but Fluss is designed for streaming analytics. How do you compare Fluss with Apache Kafka?

Kafka 75

Kafka Lambda Architecture SQL Architecture 75

Striim

AUGUST 22, 2024

Moreover, there are two forms of real-time analytics. These include: On-demand real-time analytics With on-demand real-time analytics, users send a request, such as with an SQL query, to deliver the analytics outcome. It relies on fresh data, but queries are run on an as-needed basis.

Data Warehouse 52

Data Warehouse Retail Machine Learning Database 52

Edureka

FEBRUARY 7, 2023

Dynamic data masking serves several important functions in data security. It is possible to use Azure SQL Database, Azure SQL Managed Instance and Azure Synapse Analytics. It can be set up as a security policy on all SQL Databases in an Azure subscription. 4) What is Polybase?

Data Engineering 52

Data Engineering Data Engineer Engineering Hadoop 52

Rockset

MARCH 19, 2020

With all of these stream processing and real-time data store options, though, also comes questions for when each should be used and what their pros and cons are. I hope by the end you find yourself better informed and less confused about the real-time analytics landscape and are ready to dive in to it for yourself.

Kafka 40

Kafka Database Process SQL 40

Monte Carlo

MAY 17, 2023

Upstream Code Impacting Data Systems It’s not just SQL queries that can create data quality problems. Another data team caught data freshness and volume issues caused by a bad code merge upstream. The issue was with a Lambda that took data from S3 and pushed it into Snowflake. ” 36. “Not

Data 52

Data Data Pipeline Data Engineering Data Engineer 52

Monte Carlo

MAY 17, 2023

Upstream Code Impacting Data Systems It’s not just SQL queries that can create data quality problems. Another data team caught data freshness and volume issues caused by a bad code merge upstream. The issue was with a Lambda that took data from S3 and pushed it into Snowflake. ” 36. “Not

Data Pipeline 52

Data Pipeline Data Engineering Data Engineer Data 52