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Azure Synapse

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10
Looker

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9
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Azure Synapse vs Looker: What are the differences?

Introduction

In this article, we will compare the key differences between Azure Synapse and Looker in terms of their features and functionalities.

  1. Data Integration and Processing: Azure Synapse is a cloud-based analytics service offered by Microsoft that combines enterprise data warehousing, big data integration, and data processing capabilities. It allows users to integrate and analyze data from various sources, including structured and unstructured data, in a single platform. On the other hand, Looker is a data platform that focuses on data exploration and visualization. It provides users with a user-friendly interface to explore data and create interactive dashboards and reports.

  2. Scalability and Elasticity: Azure Synapse offers seamless scalability and elasticity, enabling users to scale their data warehouse resources up or down based on demand. It can handle large volumes of data and can process parallel queries for faster analytics. Looker, on the other hand, is a scalable platform that allows users to analyze and visualize data without worrying about infrastructure management. It leverages cloud computing resources to handle varying workloads and provide consistent performance.

  3. Native Integration with Azure Services: Azure Synapse provides native integration with various Azure services, including Azure Data Lake Storage, Azure Data Factory, and Azure Machine Learning. This allows users to leverage the power of these services to enhance their data analytics capabilities. Looker, on the other hand, integrates with various data sources, databases, and cloud platforms, providing users with a unified view of their data.

  4. Advanced Analytics Capabilities: Azure Synapse offers advanced analytics capabilities, including machine learning, AI, and cognitive services. Users can build and deploy machine learning models within the platform, enabling them to gain valuable insights from their data. Looker, on the other hand, focuses on data exploration and visualization, providing users with powerful data visualization and reporting tools to analyze and present their data.

  5. Security and Compliance: Azure Synapse provides robust security and compliance features, including encryption at rest and in transit, role-based access control, and data masking. It also supports compliance with various industry standards and regulations, such as GDPR and HIPAA. Looker also offers security features such as user access controls, data encryption, and compliance with industry regulations.

  6. Integration with Existing Infrastructure: Azure Synapse allows users to seamlessly integrate with their existing on-premises infrastructure and other cloud services. It provides connectors and APIs to easily connect with various data sources and tools. Looker, on the other hand, integrates with a wide range of data sources and platforms, enabling users to leverage their existing infrastructure and tools for data analysis and visualization.

In summary, Azure Synapse and Looker are both powerful platforms for data analytics and visualization. Azure Synapse provides integrated data integration, processing, and advanced analytics capabilities with native Azure service integration. Looker, on the other hand, focuses on data exploration and visualization, providing a user-friendly interface for creating interactive dashboards and reports. Both platforms offer scalability, security, and integration capabilities, but their primary focus and feature sets vary.

Decisions about Azure Synapse and Looker

Very easy-to-use UI. Good way to make data available inside the company for analysis.

Has some built-in visualizations and can be easily integrated with other JS visualization libraries such as D3.

Can be embedded into product to provide reporting functions.

Support team are helpful.

The only complain I have is lack of API support. Hard to track changes as codes and automate report deployment.

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Vojtech Kopal
Head of Data at Mews Systems · | 3 upvotes · 303.1K views

Power BI is really easy to start with. If you have just several Excel sheets or CSV files, or you build your first automated pipeline, it is actually quite intuitive to build your first reports.

And as we have kept growing, all the additional features and tools were just there within the Azure platform and/or Office 365.

Since we started building Mews, we have already passed several milestones in becoming start up, later also a scale up company and now getting ready to grow even further, and during all these phases Power BI was just the right tool for us.

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Pros of Azure Synapse
Pros of Looker
  • 4
    ETL
  • 3
    Security
  • 2
    Serverless
  • 1
    Doesn't support cross database query
  • 4
    Real time in app customer chat support
  • 4
    GitHub integration
  • 1
    Reduces the barrier of entry to utilizing data

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Cons of Azure Synapse
Cons of Looker
  • 1
    Dictionary Size Limitation - CCI
  • 1
    Concurrency
  • 3
    Price

Sign up to add or upvote consMake informed product decisions

What is Azure Synapse?

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.

What is Looker?

We've built a unique data modeling language, connections to today's fastest analytical databases, and a service that you can deploy on any infrastructure, and explore on any device. Plus, we'll help you every step of the way.

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What are some alternatives to Azure Synapse and Looker?
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.
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.
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.
Tableau
Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click.
Snowflake
Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.
See all alternatives