The philosophy behind continuous improvement is simple: incremental and ongoing changes to your organization result in more efficient business processes, better products, and superior customer experiences. The pursuit of excellence is familiar to many of us in our daily work, and your organization may already have a culture, goal, or process management method designed to improve performance and facilitate operational excellence.
Today, data is the lifeblood of every organization. Without it, you’re left without key insights into business processes, consumers, employees, and the overall health of your entire organization. And here’s the scariest part: most organizations are on life support due to their inability to properly manage their data and turn it into actionable insights. Data fabric technology can help in a significant way, which is why it’s been getting so much buzz recently.
Unify data from diverse sources and formats using Aiven Kafka's open source streaming. Analyze with BigQuery for swift, accurate insights.
Ractor is Ruby's new Actor-like concurrency abstraction—it lets execute code in parallel without worrying about thread safety. This article is an excellent introduction to Ractors and how to begin using them in your Ruby code.
The short month of February was a time of intensive work in Loadero, and while some of the biggest updates are coming soon, we have some news about what has been added to Loadero recently. Below are the updates we’ve done.
Data fabrics are getting a lot of attention lately, and for good reason. But, for any topic with a lot of hype, there also tends to be a lot of confusion. If you are still trying to fully grasp where the concept of a data fabric architecture fits amongst all of the warehouses, lakes, lakehouses, and meshes of the data engineering world, let's set the record straight. What is a data fabric? A data fabric is a toolset that connects data across disparate sources to create a unified data model.
Monthly active rows enable data professionals and businesses of all sizes to maximize the value of Fivetran.
Recently, we announced enhanced multi-function analytics support in Cloudera Data Platform (CDP) with Apache Iceberg. Iceberg is a high-performance open table format for huge analytic data sets. It allows multiple data processing engines, such as Flink, NiFi, Spark, Hive, and Impala to access and analyze data in simple, familiar SQL tables.