As organizations adopt cloud technologies and modernize their applications, the data they generate and ingest often grows exponentially, leaving them with difficult choices for storing and using this data. Customers are beginning to explore moving away from traditional relational database management systems (RDBMS) because of the data volume to be ingested, as these RDBMS often cannot handle workloads.

For many workloads, using a time series database is a smart choice that saves time and storage space. Developers and companies have more database choices than ever. Choosing the right database for a project saves time when writing and querying data. As companies work with larger datasets to make increasingly intelligent and automated systems, efficiency is key. For many workloads, using a time series database is a smart choice that saves time and storage space.

One of the biggest mistakes I’ve had to repeatedly help companies fix has been poor partitioning design. I’ve seen many database architectures designed in an attempt to make queries faster. While faster queries can be a product of implementing partitioning correctly for a given design, I’ve often seen query response times get much slower from implementing partitioning incorrectly for the database design.

Database management systems are an essential component of business applications. Over the years, MS SQL has earned its place in the hearts of database administrators (DBAs) as the most trusted relational database management system. It is still the go-to choice for many DBAs as it helps them leverage its extensive capabilities across various dimensions such as security, portability, transaction processing and analytics.

Today, we are excited to announce that Platform.sh now offers the latest version of MongoDB to our Enterprise and Elite customers. Clients can now enjoy improved visibility via one source of control, the ability to track multiple applications and users, and native, at-rest encryption that meets the latest security compliance standards. There are more details about the benefits of MongoDB below.

Monitor Oracle DB in Google Cloud Platform with the Google Ops Agent. The Ops Agent is available on GitHub, and makes it easy to collect and ship telemetry from dozens of sources directly to your Google Cloud Platform. You can check it out here! Below are steps to get up and running quickly with observIQ’s Google Cloud Platform integrations, and monitor metrics and logs from Oracle DB in your Google Cloud Platform.

The Amazon Relational Database Service (RDS) enables Amazon Web Services (AWS) customers to manage, operate, and scale their databases. A managed service, RDS provides seven database engines and multiple instance sizes and types for working with relational databases. AWS also claims RDS can match the performance, scalability, and availability of commercial databases for a tenth of the price. Is this true, and how much does Amazon RDS cost really? This guide explains how Amazon RDS pricing works.

While working on improving the Netdata PostgreSQL collector, we were monitoring our production PostgreSQL instance and something caught our attention immediately. The rows fetched ratio seemed really, really low for one particular database… there were missing indexes in PostgreSQL! Rows fetched ratio is the percentage of rows that contain data needed to execute the query (rows fetched), out of the total number of rows scanned (rows returned).

In monitoring, the term cardinality defines the number of unique time series stored in a time series database (TSDB). The higher the cardinality, the more resources are usually required for metrics processing and querying.