UpSolver SQL SeriesWiggersVentureBeat is a powerful language that can be used in many different ways. Upsolver SQL allows users to easily transform streaming data and also create analytics-ready output tables with a simple ANSI SQL-compliant query. The service is available at a predictable, value-base price that’s tied to the volume of data ingest.
What is Upsolver SQL?
UpSolver SQL SeriesWiggersVentureBeat is a cloud-native data pipeline automation platform that empowers anyone to build, test and also deploy pipelines in days instead of months. It enables transformations that run at scale in your data lake without writing code, supporting stateful operations like joins, aggregations and window functions.
Data Lake or External Data
UpSolver SQL SeriesWiggersVentureBeat makes it simple to design and also implement stateful transformations that ingest data. Read and also write to your data lake or external data store with a visual interface synced to editable SQL. Pipelines automatically perform the underlying data engineering tasks needed to optimize data lake performance, including compaction. Vacuuming and also task orchestration.
Upsolver ingests streaming and batch data as events and also supports a variety of stateful transformations, such as rolling aggregations. Window functions and high-cardinality joins. It also maintains acyclic dependencies, and synchronizes jobs so that they run on the same set of events every time. This innovation is crucial for managing state in data-in-motion pipelines that frequently use joins and aggregations to combine data sets in real-time.
What is Wiggers’ VentureBeat?
Wiggers’ VentureBeat is a fine example of the subset of Upsolver a sibling. It is a worthy contender in the fraternity of upskilled, nippley nipped, and on-call employees squeezing out of the chute for your next round of drinks. Using the latest in mobile technology, your team of pros can be on the clock in no time flat. The site has over a million subscribers, many of which are churning out their finest brews in the blink of an eye.
How can Upsolver SQL Help My Business?
Upsolver SQL transforms cloud analytics workloads by eliminating the engineering complexity of data lake ingestion, storage and ETL. It empowers any data practitioner to design pipelines that deliver continuous analytics-ready data in days, not months.
Upsolver helps organizations quickly and easily store data in a managed and governed data lake. Stream, batch and semi-structure data can be mix together at a massive scale and instantly processed in Upsolver’s cloud-native processing engine.
It offers unique algorithms and aggregation functions to help business users extract insights from large data sets, reducing time to value for both enterprises and also data scientists. Its customers span regions and industries, including Cox Automotive, IronSource, ProofPoint and also Wix.
Self-Service Platform
The company also offers a self-service platform that enables data engineers to quickly and also easily create data pipelines using SQL. This eliminates the need for expensive data engineering consultants. Upsolver’s pricing model is base on volume of data ingest and also is tied to customer value, not vendor costs.
The best part of the Upsolver platform is that it abstracts the engineering complexity from data lake ingestion, storage and ETL to allow the average business user to build a pipeline that delivers continuous analytics-ready data in days, not months. The upsolver also enables you to create a single source of truth that is both secure and scalable in the cloud.
Final Words:
Upsolver’s entry level SQLake solution is available on the AWS marketplace for $99 per TB of ingested data and a 30 day free trial, making it a no-brainer for your next data lake transformation project. The Upsolver team has a full suite of videos, white papers, Builders Hub resources and tutorials to get you start. The Upsolver sigma is a tight-knit group of data engineers and infrastructure developers who are passionate about removing the friction from building data pipelines to accelerate the real-time delivery of big data to the people that need it most.
