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Upsolver
Upsolver

Upsolver

By Upsolver


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Highlights : About Upsolver

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Job Scheduling

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Data Analysis

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High Volume Processing

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Data Quality Control

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Pre-aggregate data in-flight

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Manage tables and partitions

About Upsolver

Upsolver is a big data analytics solution built for analyzing event streams at scale by providing a complete solution for self-service ETL, data preparation and management of streaming data within a cloud data lake architecture. The solution enables BI and data analysts to be self-sufficient when working with complex data sources such as IoT, app activity and server logs, without relying on complex data engineering pipelines to provide the data. Upsolver supports multiple input and output formats such as Avro, Parquet and Protobuf for data integration across platforms. Users can segregate data by actual event time, merge data files and split large files. The drag-and-drop interface allows managers to prepare the data for analysis.

Specifications

  Business Size
Startup
  Customization
Yes
  Deployments
Cloud
  Language Support
English
  Platforms

Product Details

Features

Data quality control

Master data management

Get data streams from Kafka / Kinesis to S3

Connect to an existing data lake or bucket on S3

Data discovery

Self service data preparation

High volume processing

Drag & drop data preparation

Data privacy

Data integration

Work with nested JSON without manual wrangling

Enrich data with out-of-the-box functions or Python snippets

Pre-aggregate data in-flight

Replay data from S3 to incorporate additional fields

Multiple input and output formats

Benefits

Upsolver offers high-performance, big data processing. Work with PBs of data from dozens of streams.

Instantly set up a data lake, data pipelines and ETL flows, and go from raw streams to structured tables in minutes using a self-service GUI and SQL.

Store data in a managed and governed data lake in the cloud (S3, Azure, Google) or on-premises (HDFS), and get immediate value from big data projects.

Upsolver eliminates the need to “glue” together different components to store, process and consume streaming data, cutting down the time-to-value.

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