Overview

Looker

Stacks636

Followers656

Votes9

Azure Synapse

Stacks103

Followers230

Votes10

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.

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.
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.
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.
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.
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.
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.

Advice on Looker, Azure Synapse

Vojtech

Head of Data at Mews

Nov 24, 2019

Decided

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.

353k views353k

Comments

Wei

CTO at Flux Work

Jan 8, 2020

Decided

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.

230k views230k

Comments

Detailed Comparison

Looker	Azure Synapse
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.	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.
Zero-lag access to data;No limits;Personalized setup and support;No uploading, warehousing, or indexing;Deploy anywhere;Works in any browser, anywhere;Personalized access points	Complete T-SQL based analytics – Generally Available; Deeply integrated Apache Spark; Hybrid data integration; Unified user experience
Statistics
Stacks 636	Stacks 103
Followers 656	Followers 230
Votes 9	Votes 10
Pros & Cons
Pros 4 GitHub integration 4 Real time in app customer chat support 1 Reduces the barrier of entry to utilizing data Cons 3 Price	Pros 4 ETL 3 Security 2 Serverless 1 Doesn't support cross database query Cons 1 Concurrency 1 Dictionary Size Limitation - CCI

What are some alternatives to Looker, 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.