Motivation: Supporting High Data Ingestion Rates and Efficient SQL queries The cost of monitoring solutions highly depends on the required footprint to ingest the monitoring data and to query these […]
If you are familiar with our previous posts, you probably know about the many disruptive features available in LeanXcale. Linear ultra-scalability, fast key-value ingestion, and online aggregations are among the […]
In previous posts, we talked about LeanXcale disruptive features, like its linear scalability, online aggregations, and bi-dimensional partitioning. In this post, we want to explore how these features can help […]
Hi! In this post, I demonstrate how to connect an R application to a LeanXcale database with the LeanXcale JDBC driver. As you may know, R is a language and […]
In this post, we continue working with Debezium and LeanXcale configured for a common client scenario that has a large database, and we want to start a replication database as […]
One important requirement many clients ask us to work on with LeanXcale is to have an instance running in parallel with their databases while not changing their existing systems. A […]
I want to share how to build a simple loader for CSV files using statistics that show the COVID-19 evolution throughout Spain. Once loaded, this data can be explored with […]
As promised, here is the continuation of the post about the Kafka connector for LeanXcale. Let’s see how to execute it! Execution To implement this example, the Kafka suite must first […]
As a crucial component of the toolset required to establish an effective operational or Big Data architecture with LeanXcale, we provide the Kafka sink connector that enables interconnection between LeanXcale […]
This article explains how LeanXcale is deployed to adjust its components to different types of workloads. LeanXcale is a database that supports multiple interfaces, such as SQL or massive ingestions […]