Azure Synapse vs Blazer

Overview

Blazer

Stacks23

Followers24

Votes0

GitHub Stars4.7K

Forks486

Azure Synapse

Stacks104

Followers230

Votes10

Blazer vs Azure Synapse: What are the differences?

What is Blazer? Share data effortlessly with your team. Works with PostgreSQL and MySQL. Share data effortlessly with your team.

What is Azure Synapse? Analytics service that brings together enterprise data warehousing and Big Data analytics. It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

Blazer can be classified as a tool in the "Business Intelligence" category, while Azure Synapse is grouped under "Big Data Tools".

Some of the features offered by Blazer are:

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

On the other hand, Azure Synapse provides the following key features:

Complete T-SQL based analytics – Generally Available
Deeply integrated Apache Spark
Hybrid data integration

Blazer is an open source tool with 2.56K GitHub stars and 324 GitHub forks. Here's a link to Blazer's open source repository on GitHub.

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Detailed Comparison

Blazer	Azure Synapse
Share data effortlessly with your team	It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.
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	Complete T-SQL based analytics – Generally Available; Deeply integrated Apache Spark; Hybrid data integration; Unified user experience
Statistics
GitHub Stars 4.7K	GitHub Stars -
GitHub Forks 486	GitHub Forks -
Stacks 23	Stacks 104
Followers 24	Followers 230
Votes 0	Votes 10
Pros & Cons
No community feedback yet	Pros 4 ETL 3 Security 2 Serverless 1 Doesn't support cross database query Cons 1 Concurrency 1 Dictionary Size Limitation - CCI
Integrations
PostgreSQL MySQL	No integrations available

What are some alternatives to Blazer, Azure Synapse?

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.

Google BigQuery

Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

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.

Amazon Redshift

It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Presto

Distributed SQL Query Engine for Big Data

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

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.