If you’re just joining us, I highly advise you to go back and check out our first two parts of this three part series regarding NGINX and Sumo Logic where we go over a basic Introduction to NGNIX and also Touch Up On NGINX, Logs, and Why Logs Are Important. If you’ve been following along, then great, let’s jump right into it.
This is a guest post by Noah Zoschke, Engineering Manager at Segment. Segment is the customer data infrastructure that makes it easy for companies to clean, collect, and control their first-party customer data. At Segment, our ultimate goal is to collect data from Sources (e.g., a website or mobile app) and route it to one or more Destinations (e.g., Google Analytics and AWS Redshift) as quickly and reliably as possible.
Apache Hive is an open source interface that allows users to query and analyze distributed datasets using SQL commands. Hive compiles SQL commands into an execution plan, which it then runs against your Hadoop deployment. You can customize Hive by using a number of pluggable components (e.g., HDFS and HBase for storage, Spark and MapReduce for execution). With our new integration, you can monitor Hive metrics and logs in context with the rest of your big data infrastructure.
Exoprise recently released two new CloudReady sensors for monitoring Transport Layer Security (TLS), aka Secure Sockets Layer (SSL), connections end-to-end. TLS/SSL is the foundation for just about every web request and transaction across the Internet today. Arguably, SSL is as important as TCP/IP itself to the formation of our modern-day Internet, SaaS and Cloud world.
Recently, a Mattermost customer needed a way to provide Mattermost access to team members of newly acquired business entities. After reviewing the security posture of the companies they acquired, they realized they all had different policies, tools, and systems and needed to bring them all up to the same standards and create an easy to manage multi-authentication solution. The solution they implemented gave the organization a singular login and multi-factor authentication service.
Starting this week, we will do a series of four blogposts on the intersection of Spark with Kubernetes. The first blog post will delve into the reasons why both platforms should be integrated. The second will deep-dive into Spark/K8s integration. The third will discuss usecases for Serverless and Big Data Analytics. The last post will round off with insights on best practices.
Incidents happen all the time because of bad code deploys. You write some code that passes code review, it then is automatically shipped to production after a test suite passes, and BAM, an outage happens. This fairly common occurrence has ways to prevent it entirely. Using some simple ideas we can defend ourselves from the hidden mistakes that code reviews and chaos engineering sometimes won’t catch.
