The growth of data has been exponential. By 2023, it's anticipated that approximately 463 exabytes (EB) will be created every day. To put this into perspective, one exabyte is a unit equivalent to 1 billion gigabytes. By 2021, 320 billion emails will be sent daily, many of which contain personal information. Data collected around the globe contains the type of information that businesses leverage to make more informed decisions.
Building a data lake in Amazon S3 using AWS Spectrum to query the data from a Redshift cluster is a common practice. However, when it comes to boosting performance, there are some tricks that are worth learning. One of those is using data in Parquet format, which Redshift considers a best practice. Here's how to use Parquet format with Xplenty for the best data lake performance.
These days, there are two kinds of businesses: data-driven organizations; and companies that are about to go bust. And often, the only difference is the data stack. Data quality is an existential issue—to survive, you need a fast, reliable flow of information. The data stack is the entire collection of technologies that make this possible. Let's take a look at how any company can assemble a data stack that's ready for the future.
The component previewer is a feature that allows you to preview your data at each component step without having to validate packages and run full-scale production jobs. It gives you the ability to extract, transform and preview your data on any transformation component, allowing you to debug your pipeline and/or to confirm and validate your data flow logic. Component previews are similar to the data previews available on source components, which you might already be familiar with.
One of the most requested features in a data integration tool is greater flexibility around the scheduling of packages and workflows. With Xplenty, this can be achieved through the use of our Cron Expression scheduling feature. Cron is a software utility that enables Unix-based operation systems, such as Linux, to use a job scheduler. You can create cron jobs, which execute a script or command at a time of your choosing. Cron has broad applications for tasks that need time-based automation.
AWS provides many solutions for managing business data. There’s Amazon Relational Database, or Amazon RDS, which is ideal for scaling your databases on the cloud. There’s Amazon Redshift for warehousing your data. For collecting big data, we’ve looked at a number of modern data integration platforms, but Amazon CloudFront is more of a content delivery platform. So, why are we talking about CloudFront in terms of big data right now?
Five differences between Stitch vs. Dell Boomi vs. Xplenty: Real-time data provides a competitive advantage, so every business requires an analytics strategy. But many organizations struggle to integrate data because they store information in lots of locations, including apps, SaaS, and legacy systems. Extract, Transform, and Load (ELT) makes it easier for companies like yours to access data in disparate locations and move it to one centralized system.
The amount of big data generated around the world by the time you finish this page is limitless. Think about it for a second. Companies everywhere will create an innumerable amount of data right now — customer records, sales orders, chain reports, emails, you name it. Companies need all this data for data analytics — the science of modeling raw data to uncover precious real-time insights about their business. It's like opening a treasure trove.
With 42 percent of Americans still working from home, we're using TVs for more than just Netflix. These viewing screens double up as dynamic digital dashboards, displaying powerful SaaS metrics that power your business. Whether you're working from home or the office, turning your TV into a SaaS KPI dashboard is simple. Five tools are all you need. This guide shows you how to bring business metrics to the small screen with your very own custom SaaS KPI dashboard.
Version Control is a critical component of any software development team, particularly if you're collaborating with a large group of individuals. When done right, Version Control will help you track changes over time, like scheduling specific versions to accommodate the development of new features and bug fixes. You can even rollback to a specific version with ease as you continue testing.
