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Control Your Data Flow with Ingest Budgets

We are pleased to announce the release of Ingest Budgets, a new feature which enables our users to track and control how much data is ingested into Sumo Logic and avoid overages in environments where data ingestion can spike unexpectedly. With Ingest Budgets, users can create budgets with ingestion thresholds that either cap data ingestion to a daily limit or simply alert whenever the threshold is exceeded.

Symfony Performance Improvements: Tips and Techniques

Perhaps you came upon this post while looking at ways to improve Symfony performance. Or maybe you read our comparison of Laravel and Symfony and want to know more. You could have gotten here because you want to write a performant app from the start. Then again, you could just love reading all of Stackify’s blog posts. And who could blame you? However you got to this post, or whatever goals you may have, I’m here to talk to you about Symfony performance tuning.

How to Visualize Data that Really Matters to Business with Grafana and MySQL

So you have a Grafana dashboard that shows failures at 0.01% and that latency is down throughout the company. But rather than get a pat on the back, “your boss’s boss is saying cut the crap or stop the mumbo jumbo. What does it really mean for our business?” said Peter Zaitsev, CEO of Percona, which offers solutions such as support, management services, consultant training, and custom engineering for MySQL, MariaDB, MongoDB, Postgres and other open source databases.

Key Insights From Forrester Research on Containers and Microservices

In our previous post about containers and microservices, we covered the challenges of monitoring these new technologies as well as how you can use software-defined IT operations tools to overcome them. The recently released Forrester Research report, Monitoring Containerized Microservices?

Use Kubernetes to Speed Machine Learning Development

As industries shift to a microservices approach of deploying applications using containers, data scientists can reap the benefits. Data Scientists use specific frameworks and operating systems that can often conflict with the requirements of a production system. This has led to many clashes between IT and R&D departments. IT is not going to change the OS to meet the needs of a model that needs a specific framework that won’t run on RHEL 7.2.