If you're looking to lower storage costs by compressing your data and get better query performance when querying the data in Cloud Object Storage, you may want to click to learn how to convert CSV objects to Parquet.
IBM Cloud SQL Query lets you use standard SQL and Apache Spark SQL functions to query your structured and semi-structured data stored in Cloud Object Storage (COS). It’s a serverless solution that makes it easy to analyze lots of data in COS by pointing to the COS bucket that stores your data.
In this third part of a four-part series on Operationalizing SQL Query, we'll bring together the microservices we deployed in Part 1 to query data in IBM Cloud Object Storage (COS) using the techniques we developed in Part 2 using IBM SQL Query with the goal of connecting our application's data to Business Intelligence (BI) tools.
Operationalizing IBM SQL Query: Part 2. In this article, we'll take a look at the best practices for connecting to IBM Cloud Object Storage from docker containers deployed in the IBM Cloud Kubernetes Service.
Geospatial data plays a crucial role in data forecasting, spatial analytics, and reporting, especially in the fields of logistics and finance. With IBM Cloud SQL Query, you can now run SQL queries on geospatial data on files stored as CSV, Parquet, or JSON in IBM Cloud Object Storage (COS) using IBM's geospatial toolkit.
When you have vast quantities of rectangular data, the way you lay it out on object storage systems like IBM Cloud Object Storage (COS) makes a big difference to both cost and performance of SQL queries. However, this task is not as simple as it sounds. Here we survey some tricks of the trade.