In this blog post, we’ll explore how Kroger is making the right digital transformation investments using a modern data layer to power a flexible growth engine for their business.
Temenos could not continue to rely on monolithic databases for manufacturing business operations. They needed a high-availability, scale-out transactional database, so they turned to YugabyteDB. They recently announced that the Temenos Banking Cloud is achieving 100,000 business transactions per second. Learn how that performance benchmark was achieved.
In this blog post, we will stream data to downstream databases leveraging YugabyteDB’s Change Data Capture (CDC) feature introduced in YugabyteDB 2.13. This will publish the changes to Kafka and then stream those changes to databases like MySQL, PostgreSQL, and Elasticsearch.
This post walks through how to send data from YugabyteDB to Elasticsearch using YugabyteDB’s Change Data Capture (CDC) feature.
This post describes how we can send data from YugabyteDB to ClickHouse through YugabyteDB’s Change Data Capture (CDC) feature.
In this post, we explore YugabyteDB’s pull-based approach to CDC introduced in YugabyteDB 2.13 that scans changes from the database’s write-ahead-log (WAL).
YugabyteDB is a 100% open source, distributed SQL database system. This single phrase expresses two distinct notions: a SQL database system, and a distributed database system. Historically, these notions were mutually exclusive. But current technology allows a single system to implement both notions. YugabyteDB does this with its two-layer architecture: an extensible query processing layer and a distributed document store.
In this blog post,
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In this post, we demonstrate how business users can use Apache Superset to get valuable insights from their data stored in YugabyteDB. We’ll also walk through a retail operations use case that keeps track of order processing efficiency in a specific business territory, as highlighted below.
In the second part of this blog series on PostgreSQL and YSQL date-time data types, the focus shifts to representing durations, or how long things last. Assuming the reader has read the first part and downloaded the companion code kit, this post explores the relevant data types, including interval, timestamp difference, and other related functions.
This is the first of a two-part blog post series that deals with the basic business of representing moments using time, date, and timestamp data types. YugabyteDB’s YSQL subsystem provides a similar experience to PostgreSQL, so the topic may still be enlightening for some YSQL users.
