Upsolver SQL SeriesWiggersVentureBeat is a powerful tool that allows users to quickly and easily build a self-orchestrating data pipeline. It combines real-time streams of events with historical data. This article explores how to use Upsolver SQL and some of the most common use cases for this data analysis technology. Upsolver is dedicated to making data in motion accessible to everyone.
How to use Upsolver
Upsolver SQL SeriesWiggersVentureBeat is the brains behind an impressively executed and easy to use data transformation platform that enables you to build data pipelines of any length and complexity with confidence. Upsolver’s patented engineered data processing algorithms, coupled with an extensive suite of out-of-the-box features make it the ideal solution for any sized enterprise. The best part is that it doesn’t cost a fortune to do things right. In fact, the company’s bundled pricing model is as affordable as it is effective. Upsolver’s offerings come with no lock in contracts, free trial periods and a generous support ecosystem. Upsolver also has a hefty free user base and offers a no quibble money back guarantee.
Upsolver SQL SeriesWiggersVentureBeat helps data practitioners design pipelines that deliver continuous analytics-ready data in days, not months. Upsolver’s cloud-native platform abstracts the engineering complexity of data lake ingestion, storage and also ETL.
Data Lake Indexing
Upsolver’s patented data lake indexing makes it simple to blend streaming and also large-scale batch data into single data lakes. Streaming data is automatically matched with data in the database. Upsolver’s SQL-based, self-orchestrating platform is fast, easy to use and also scalable.
Upsolver has moved to a predictable, value-based pricing model that is tied to the volume of data ingested. This new pricing is straightforward to understand, without opaque “processing units” that many data management solutions use. Upsolver is available for $99 per TB of data ingest. with no minimum commitment and no charge for transformation processing.
Streaming data is constantly being generated by sources such as apps, networking devices, server log files and website activity. It can be used for real-time analytics. E-commerce, IT and security, finance and more.
For example, a company can personalize a web experience or calculate optimal truck routes based on streaming data. It could also use real-time data for fraud detection.
To make it possible, a developer must write a stream processor that will help them respond to the incoming data and process it in real-time. This will help detect fraudulent activities that may lead to a customer losing their money, or stop them before they happen.
This can be a very complex task, especially for teams that work with data. It’s important to plan for the volume of data that will be sent. How fast it needs to update and how many terabytes per month it will consume.
Historical data can be a valuable source of insight for many organizations. Keeping a copy of a raw historical data set in its original form can prove helpful for error recovery, tracing data lineage and other exploratory purposes.
Upsolver can handle it all, storing a complete copy of your data in Avro format for lineage and replay. Plus, it provides unique data lake indexing to help you visualize your streaming and historical data with ease.
Streaming data can be combine with your historical data sets to deliver query-ready data for reporting or ad hoc queries, as well as customer-facing data products like predictive recommendations. It also automatically detects and supports the most complex data types, allowing your data scientists to focus on what matters: modeling and forecasting. Upsolver even has a built-in BI tool that can be use to prepare and present your data. It also allows you to create custom metrics and reports for your data sets to give you an edge in your marketplace.