If you’re using Elasticsearch as part of an ELK stack solution for log analytics, you’ll need to manage the size of your indexed log data to achieve the best performance. Elasticsearch indices have no limit to their size, but a larger index takes longer to query and is more costly to store. Performance degradation is often observed with large Elastic indices and queries on large indices can even crash Elasticsearch when they use up all of the available heap memory on the node.

With more than 150,000 customers and millions of users, Salesforce processes a lot of data — accounts, contacts, activities, leads, opportunities, you name it. And people are adding more data to Salesforce all the time. As organizations realize the benefits of customer relations, Salesforce has become the number one destination for sales, marketing, eCommerce, and field service data. That's even more accounts, contacts, activities, leads, and opportunities.

Data is everywhere. As the sheer volume and number of data sources continue to explode, so do new opportunities for modern businesses to create and act on insights. That is if they are equipped with the right analytics technology. Historically, many businesses have settled for “good enough” analytics tools, putting up with lackluster bundles from full-stack vendors in an attempt to minimize cost or risk.

Python is one of the most in-demand programming languages in the world — and for good reason. Knowing how to code has never been so valuable thanks to the expanding world of tech and focus on data science. From landing high-paying jobs to improving your skillset, learning Python scripting can bring you many opportunities to succeed. However, while these opportunities are robust, many challenges come with learning Python.

Databox’s Benchmarks feature allows you to see how your company compares to others like yours and helps you set better strategy and business goals.

27 experts share the top data quality issues that they’ve struggled with and how to solve them for better data reporting.

Change data capture (CDC) is a crucial, but also tremendously underappreciated, feature that forms the backbone of modern ETL workloads. Without knowing which data has changed since you last accessed it, you’d be forced to extract all the data from a source table or database each time that you perform data integration—which would be a tremendously inefficient process.

Since the release of Cloudera Data Engineering (CDE) more than a year ago, our number one goal was operationalizing Spark pipelines at scale with first class tooling designed to streamline automation and observability. In working with thousands of customers deploying Spark applications, we saw significant challenges with managing Spark as well as automating, delivering, and optimizing secure data pipelines.

“Water, water, everywhere, nor any drop to drink.” The famous line from Samuel Taylor Coleridge’s epic poem “The Rime of the Ancient Mariner” has a fitting application to today’s data problem. Enterprises are deluged with data, but they often have no way to leverage it. According to most experts, only a small percentage of data is usable and made useful, and most of it is in the dark — thus the term, “dark data.”

Out with the old; in with the new! If you haven’t already checked out the new Snowflake® interface (aka Snowsight®), make it your New Year’s resolution. Set yourself up for success in 2022 by spending a few minutes getting to know the new features and experiences that are in public preview—available when you click the Snowsight button at the top of your console’s menu bar.