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  5. Azure Synapse vs Microsoft SSRS

Azure Synapse vs Microsoft SSRS

OverviewComparisonAlternatives

Overview

Microsoft SSRS
Microsoft SSRS
Stacks96
Followers138
Votes0
Azure Synapse
Azure Synapse
Stacks104
Followers230
Votes10

Azure Synapse vs Microsoft SSRS: What are the differences?

  1. 1. Data Storage and Processing Capabilities: Azure Synapse is a fully integrated analytics service that combines big data and data warehousing capabilities into one solution, whereas Microsoft SSRS (SQL Server Reporting Services) is primarily designed for creating, managing, and delivering traditional paginated reports.
  2. 2. Real-Time Data Processing: Azure Synapse supports real-time data processing by allowing users to ingest, enrich, and analyze streaming data using built-in connectors and integration with popular services like Azure Event Hubs and Apache Kafka. On the other hand, Microsoft SSRS is not designed for real-time data processing and focuses more on generating static reports based on historical data.
  3. 3. Advanced Analytics and Machine Learning: With Azure Synapse, users can take advantage of integrated advanced analytics and machine learning capabilities provided by Azure Machine Learning. This enables the development and deployment of predictive models and the execution of machine learning algorithms on large-scale datasets. In comparison, Microsoft SSRS does not offer built-in advanced analytics and machine learning functionality.
  4. 4. Scalability and Elasticity: Azure Synapse is built to scale effortlessly and provides elastic scalability by automatically allocating resources as per the workload requirements. This ensures high performance even with large and complex datasets. On the other hand, Microsoft SSRS is limited by the resources of the server it is hosted on and may require manual resource allocation for scaling.
  5. 5. Data Integration and Orchestration: Azure Synapse offers robust data integration and orchestration capabilities by providing a unified and integrated environment for data ingestion, storage, data preparation, and data movement. It supports seamless integration with other Azure services like Azure Data Factory, Azure Databricks, and Azure Logic Apps. While Microsoft SSRS focuses solely on reporting and lacks comprehensive data integration and orchestration features.
  6. 6. Cost Model and Pricing: Azure Synapse follows a consumption-based pricing model where users are billed based on the resources they provision and the actual usage. It offers different pricing tiers to cater to different needs. In contrast, Microsoft SSRS is typically licensed as part of SQL Server and follows a traditional licensing model based on the number of users or processors.

In Summary, Azure Synapse and Microsoft SSRS differ in terms of their data storage and processing capabilities, support for real-time data processing, advanced analytics and machine learning functionality, scalability and elasticity, data integration and orchestration capabilities, as well as their cost model and pricing.

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Detailed Comparison

Microsoft SSRS
Microsoft SSRS
Azure Synapse
Azure Synapse

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.

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.

"Traditional" paginated reports; New mobile reports; A modern web portal
Complete T-SQL based analytics – Generally Available; Deeply integrated Apache Spark; Hybrid data integration; Unified user experience
Statistics
Stacks
96
Stacks
104
Followers
138
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
Microsoft SQL Server
Microsoft SQL Server
No integrations available

What are some alternatives to Microsoft SSRS, Azure Synapse?

Metabase

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

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

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

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

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

Presto

Presto

Distributed SQL Query Engine for Big Data

Amazon EMR

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

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

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

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.

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