Azure Synapse vs Google Datastudio

Overview

Google Datastudio

Stacks202

Followers170

Votes16

Azure Synapse

Stacks104

Followers230

Votes10

Google Datastudio vs Azure Synapse: What are the differences?

Azure Synapse and Google Data Studio are both powerful data analytics and visualization platforms. Here are the key differences between Azure Synapse and Google Data Studio:

Purpose and Target Users: Azure Synapse is an integrated analytics service provided by Microsoft Azure. It is designed for large-scale data warehousing and data integration scenarios, supporting both analytical processing (SQL) and big data processing (Apache Spark). Azure Synapse is geared toward data engineers, data scientists, and business analysts. On the other hand, Google Data Studio is a cloud-based data visualization tool offered by Google Cloud Platform. It is built for business users, marketers, and analysts who want to create interactive and visually appealing reports and dashboards using various data sources.
Data Processing and Integration: Azure Synapse provides a unified and scalable platform for data integration, data warehousing, and big data analytics. It supports seamless data ingestion from various sources and enables data transformations and modeling using SQL-based queries and Spark jobs. Azure Synapse also integrates closely with other Azure services, such as Azure Data Lake Storage and Azure Machine Learning. In contrast, Google Data Studio primarily focuses on data visualization and reporting. It connects to a wide range of data sources, including Google Analytics, Google Sheets, and various third-party data connectors, but its primary emphasis is on creating interactive and shareable dashboards and reports.
Advanced Analytics and Machine Learning: Azure Synapse offers built-in capabilities for machine learning through integration with Azure Machine Learning. It allows users to develop and deploy machine learning models directly from the Synapse workspace, enabling advanced analytics on large datasets. Google Data Studio, on the other hand, lacks native machine-learning capabilities and is primarily focused on data visualization and reporting.
Platform and Ecosystem: Azure Synapse is part of the Microsoft Azure ecosystem, providing seamless integration with other Azure services, such as Azure Data Factory, Azure Databricks, and Power BI. Google Data Studio is part of the Google Cloud Platform, and while it integrates well with other Google services like Google Analytics and Google Sheets, its ecosystem is more limited compared to Azure's.

In summary, Azure Synapse is a comprehensive data analytics platform tailored for large-scale data warehousing, data integration, and big data analytics, targeting data engineers and data scientists. It offers advanced analytics capabilities and seamless integration with the Microsoft Azure ecosystem. Google Data Studio, on the other hand, is a user-friendly data visualization tool designed for business users and analysts, focusing on creating visually appealing reports and dashboards with various data sources, primarily within the Google Cloud Platform ecosystem.

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Detailed Comparison

Google Datastudio	Azure Synapse
It lets you create reports and data visualizations. Data Sources are reusable components that connect a report to your data, such as Google Analytics, Google Sheets, Google AdWords and so forth. You can unlock the power of your data with interactive dashboards and engaging reports that inspire smarter business decisions.	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.
Easily access a wide variety of data. Data Studio’s built-in and partner connectors makes it possible to connect to virtually any kind of data Turn your data into compelling stories of data visualization art. Quickly build interactive reports and dashboards with Data Studio’s web based reporting tools Share your reports and dashboards with individuals, teams, or the world. Collaborate in real time. Embed your report on any web page	Complete T-SQL based analytics – Generally Available; Deeply integrated Apache Spark; Hybrid data integration; Unified user experience
Statistics
Stacks 202	Stacks 104
Followers 170	Followers 230
Votes 16	Votes 10
Pros & Cons
Pros 6 Free 4 Underrated 2 Easy to share 1 Google Analytics Integration 1 Shareable & editable dashboards Cons 1 Works well with google (not aws or azure)	Pros 4 ETL 3 Security 2 Serverless 1 Doesn't support cross database query Cons 1 Concurrency 1 Dictionary Size Limitation - CCI
Integrations
MySQL Microsoft SQL Server Microsoft Excel	No integrations available

What are some alternatives to Google Datastudio, 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.

Related Comparisons

Purpose and Target Users: Azure Synapse is an integrated analytics service provided by Microsoft Azure. It is designed for large-scale data warehousing and data integration scenarios, supporting both analytical processing (SQL) and big data processing (Apache Spark). Azure Synapse is geared toward data engineers, data scientists, and business analysts. On the other hand, Google Data Studio is a cloud-based data visualization tool offered by Google Cloud Platform. It is built for business users, marketers, and analysts who want to create interactive and visually appealing reports and dashboards using various data sources.
Data Processing and Integration: Azure Synapse provides a unified and scalable platform for data integration, data warehousing, and big data analytics. It supports seamless data ingestion from various sources and enables data transformations and modeling using SQL-based queries and Spark jobs. Azure Synapse also integrates closely with other Azure services, such as Azure Data Lake Storage and Azure Machine Learning. In contrast, Google Data Studio primarily focuses on data visualization and reporting. It connects to a wide range of data sources, including Google Analytics, Google Sheets, and various third-party data connectors, but its primary emphasis is on creating interactive and shareable dashboards and reports.
Advanced Analytics and Machine Learning: Azure Synapse offers built-in capabilities for machine learning through integration with Azure Machine Learning. It allows users to develop and deploy machine learning models directly from the Synapse workspace, enabling advanced analytics on large datasets. Google Data Studio, on the other hand, lacks native machine-learning capabilities and is primarily focused on data visualization and reporting.
Platform and Ecosystem: Azure Synapse is part of the Microsoft Azure ecosystem, providing seamless integration with other Azure services, such as Azure Data Factory, Azure Databricks, and Power BI. Google Data Studio is part of the Google Cloud Platform, and while it integrates well with other Google services like Google Analytics and Google Sheets, its ecosystem is more limited compared to Azure's.

Azure Synapse vs Google Datastudio