The ELK Stack, which traditionally consisted of three main components — Elasticsearch, Logstash and Kibana, has long departed from this composition and can now also be used in conjunction with a fourth element called “Beats” — a family of log shippers for different use cases. It is this departure that has led to the stack being renamed as the Elastic Stack.

Sometimes developers are hesitant to include logging due to performance concerns, but is this justified? And how much does library choice affect performance? The team at SolarWinds® Loggly® and myself decided to find out. We ran a series of performance tests on some of the most popular Node.js libraries. These tests are designed to show how quickly each library processed logging and the impact on the overall application. Let’s see how they did!

Serverless architectures such as AWS Lambda have created new challenges in debugging code. Without a solid logging framework in place, you could waste hours, or even days, tracking down simple defects in your functions. A strategic logging framework can be a powerful way to track down and resolve bugs. Let’s walk through how to get the most out of logging Lambda functions.

On February 3, 2019, the Sumo Logic platform experienced its biggest ever spike in incoming data and analytics usage in the company’s history. On this day, close to everybody in the U.S., and many more people across the world, experienced a massive sports event: Super Bowl LIII. The spike was caused by viewers across the world tuning into the football game using online streaming video.

Logstash is the “L” in the ELK Stack — the world’s most popular log analysis platform and is responsible for aggregating data from different sources, processing it, and sending it down the pipeline, usually to be directly indexed in Elasticsearch. Logstash can pull from almost any data source using input plugins, apply a wide variety of data transformations and enhancements using filter plugins, and ship the data to a large number of destinations using output plugins.

Logs are unpredictable. Following a production incident, and precisely when you need them the most, logs can suddenly surge and overwhelm your logging infrastructure. To protect Logstash and Elasticsearch against such data bursts, users deploy buffering mechanisms to act as message brokers. Apache Kafka is the most common broker solution deployed together the ELK Stack.

If you have had any exposure to cloud computing or app development in recent years, you likely have heard the term “cloud native” thrown around. But you might be wondering what exactly that term means, and how it differs from concepts such as “cloud ready” or “cloud enabled.” As a cloud-native service provider, Sumo Logic understands the architecture underpinning this development model. Let’s take a closer look at the cloud-native concept and what it means.

Today I have the immense privilege of sharing the exciting news that we have raised $52M in series D funding led by General Catalyst. I am thrilled that all of our existing investors share our vision and chose to invest further in the company.

Elasticsearch is the living heart of what is today’s the most popular log analytics platform — the ELK Stack (Elasticsearch, Logstash and Kibana). The role played by Elasticsearch is so central that it has become synonymous with the name of the stack itself. Used primarily for search and log analysis, Elasticsearch is today one of the most popular database systems available today.

Cloud computing is well past the emerging stage. It’s no longer a radical idea for businesses to depend on cloud platforms and services to serve as their technology backbone--and the numbers show it. In 2018, Forrester reported that nearly 60% of North American enterprises rely on public cloud platforms. This year, Gartner projects that the public cloud services market will grow from last year’s $182.4 billion to $214.3 billion this year, a 17.5% jump.