AtScale vs Microsoft SQL Server Reporting Services

Overview

Microsoft SQL Server Reporting Services

Stacks44

Followers30

Votes0

AtScale

Stacks25

Followers83

Votes0

AtScale vs Microsoft SQL Server Reporting Services: What are the differences?

Developers describe AtScale as "The virtual data warehouse for the modern enterprise". Its Virtual Data Warehouse delivers performance, security and agility to exceed the demands of modern-day operational analytics. On the other hand, Microsoft SQL Server Reporting Services is detailed as "A set of on-premises tools and services that create, deploy, and manage mobile and paginated reports". It provides a set of on-premises tools and services that create, deploy, and manage mobile and paginated reports. It delivers the right information to the right users.

AtScale belongs to "Big Data Tools" category of the tech stack, while Microsoft SQL Server Reporting Services can be primarily classified under "Business Intelligence".

Some of the features offered by AtScale are:

Multiple SQL-on-Hadoop Engine Support
Access Data Where it Lays
Built-in Support for Complex Data Types

On the other hand, Microsoft SQL Server Reporting Services provides the following key features:

"Traditional" paginated reports
New mobile reports
A modern web portal

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Detailed Comparison

Microsoft SQL Server Reporting Services	AtScale
It provides a set of on-premises tools and services that create, deploy, and manage mobile and paginated reports. It delivers the right information to the right users.	Its Virtual Data Warehouse delivers performance, security and agility to exceed the demands of modern-day operational analytics.
"Traditional" paginated reports; New mobile reports; A modern web portal	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
Stacks 44	Stacks 25
Followers 30	Followers 83
Votes 0	Votes 0
Integrations
Microsoft SQL Server	Python Amazon S3 Tableau Power BI Qlik Sense Azure Database for PostgreSQL

What are some alternatives to Microsoft SQL Server Reporting Services, 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.