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Data warehousing aggregates data from disparate sources so you can run real-time reports for greater business intelligence. However, a data warehouse does more than generate big data analytics. How about using it as a data source rather than just a destination? You can move data from your warehouse to other systems in your networks, such as Salesforce or Zendesk, and improve existing operations.

Want to supercharge your HubSpot reporting? This article covers everything you need to know on how to make it simpler and more efficient.

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.

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.

Here’s a hands-on guide for creating your own marketing strategy report. Plus, 5 free-to-download dashboard templates for marketing reporting.

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.

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.

With interest in big data and cloud increasing around the same time, it wasn’t long until big data began being deployed in the cloud. Big data comes with some challenges when deployed in traditional, on-premises settings. There’s significant operational complexity, and, worst of all, scaling deployments to meet the continued exponential growth of data is difficult, time-consuming, and costly.

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.

“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.”