AtScale vs Blazer
Overview
Share your Stack
Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.
CLI (Node.js)
Manual
Detailed Comparison
Share data effortlessly with your team | Its Virtual Data Warehouse delivers performance, security and agility to exceed the demands of modern-day operational analytics. |
Secure - works with your authentication system;Variables - run the same queries with different values;Linked Columns - link to other pages in your apps or around the web;Smart Columns - get the data you want without all the joins;Smart Variables - no need to remember ids;Charts - visualize the data;Audits - all queries are tracked | Multiple SQL-on-Hadoop Engine Support;
Access Data Where it Lays;
Built-in Support for Complex Data Types;
Single Drop-in Gateway Node Deployment |
Statistics | |
GitHub Stars 4.7K | GitHub Stars - |
GitHub Forks 486 | GitHub Forks - |
Stacks 23 | Stacks 25 |
Followers 24 | Followers 83 |
Votes 0 | Votes 0 |
Integrations | |
What are some alternatives to Blazer, AtScale?
Metabase
It is an easy way to generate charts and dashboards, ask simple ad hoc queries without using SQL, and see detailed information about rows in your Database. You can set it up in under 5 minutes, and then give yourself and others a place to ask simple questions and understand the data your application is generating.
Apache Spark
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
Presto
Distributed SQL Query Engine for Big Data
Amazon Athena
Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.
Superset
Superset's main goal is to make it easy to slice, dice and visualize data. It empowers users to perform analytics at the speed of thought.
Apache Flink
Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.
lakeFS
It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.
Druid
Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.
Cube
Cube: the universal semantic layer that makes it easy to connect BI silos, embed analytics, and power your data apps and AI with context.
Power BI
It aims to provide interactive visualizations and business intelligence capabilities with an interface simple enough for end users to create their own reports and dashboards.
