Overview

Dremio

Stacks116

Followers348

Votes8

Azure Synapse

Stacks104

Followers230

Votes10

Azure Synapse vs Dremio: What are the differences?

Introduction

In the world of big data analytics, Azure Synapse and Dremio are two powerful tools that offer efficient processing and analysis capabilities. While both platforms aim to enhance data-driven decision-making, they differ in several key aspects.

Integration with Azure Ecosystem: Azure Synapse is a data integration and analytics service offered by Microsoft Azure, enabling seamless integration with other Azure services such as Azure Data Lake Storage and Azure Machine Learning. On the other hand, Dremio is an open-source data lake engine that supports integration with various cloud data storage providers, including Azure.
Data Virtualization and Caching: Azure Synapse utilizes data virtualization and caching techniques to provide real-time access to data from various sources. It allows users to query and analyze data without physically moving or replicating it. In contrast, Dremio also offers data virtualization, but with the additional capability of in-memory caching. This caching mechanism significantly improves query performance by storing frequently accessed data in memory.
Data Governance and Security: Azure Synapse emphasizes data governance and security, offering features such as Azure Active Directory integration, role-based access control (RBAC), and data classification. These features ensure that data is protected and accessed only by authorized users. Dremio, on the other hand, provides basic security measures like user authentication and authorization but does not offer advanced governance features like Azure Synapse.
Scalability and Performance: Azure Synapse provides unlimited scalability, allowing users to scale resources up or down based on demand. It harnesses the power of parallel processing to handle large volumes of data efficiently. Dremio also offers scalability but focuses more on query optimization to enhance performance. It leverages various optimization techniques to speed up query execution, such as query planning, execution plan caching, and vectorized query execution.
Data Transformation and Preparation: Azure Synapse offers comprehensive data transformation and preparation capabilities through its integrated Apache Spark engine. Users can perform advanced analytics, machine learning, and ETL (Extract, Transform, Load) operations seamlessly. Dremio, on the other hand, is primarily built for interactive data exploration and analysis and does not provide extensive data transformation features like Azure Synapse.
Ease of Use and Learning Curve: Azure Synapse provides a user-friendly interface with visual tools like Azure Synapse Studio, making it easier for users to perform complex data analytics tasks. It also offers native integration with popular data visualization tools like Power BI. Dremio, although relatively easy to use, requires some level of technical expertise to set up and configure, especially for on-premises or self-managed deployments.

In summary, Azure Synapse and Dremio differ in their integration with the Azure ecosystem, data virtualization and caching capabilities, data governance and security features, scalability and performance optimizations, data transformation and preparation functionalities, and ease of use. Both platforms have their strengths and are suitable for different use cases, depending on specific requirements and preferences.

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Advice on Dremio, Azure Synapse

karunakaran

Consultant

Jun 26, 2020

Needs advice

I am trying to build a data lake by pulling data from multiple data sources ( custom-built tools, excel files, CSV files, etc) and use the data lake to generate dashboards.

My question is which is the best tool to do the following:

Create pipelines to ingest the data from multiple sources into the data lake
Help me in aggregating and filtering data available in the data lake.
Create new reports by combining different data elements from the data lake.

I need to use only open-source tools for this activity.

I appreciate your valuable inputs and suggestions. Thanks in Advance.

80.4k views80.4k

Comments

datocrats-org

Jul 29, 2020

Needs adviceon

Amazon EC2

Tableau

PowerBI

We need to perform ETL from several databases into a data warehouse or data lake. We want to

keep raw and transformed data available to users to draft their own queries efficiently
give users the ability to give custom permissions and SSO
move between open-source on-premises development and cloud-based production environments

We want to use inexpensive Amazon EC2 instances only on medium-sized data set 16GB to 32GB feeding into Tableau Server or PowerBI for reporting and data analysis purposes.

319k views319k

Comments

Detailed Comparison

Dremio	Azure Synapse
Dremio—the data lake engine, operationalizes your data lake storage and speeds your analytics processes with a high-performance and high-efficiency query engine while also democratizing data access for data scientists and analysts.	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.
Democratize all your data; Make your data engineers more productive; Accelerate your favorite tools; Self service, for everybody	Complete T-SQL based analytics – Generally Available; Deeply integrated Apache Spark; Hybrid data integration; Unified user experience
Statistics
Stacks 116	Stacks 104
Followers 348	Followers 230
Votes 8	Votes 10
Pros & Cons
Pros 3 Nice GUI to enable more people to work with Data 2 Easier to Deploy 2 Connect NoSQL databases with RDBMS 1 Free Cons 1 Works only on Iceberg structured data	Pros 4 ETL 3 Security 2 Serverless 1 Doesn't support cross database query Cons 1 Dictionary Size Limitation - CCI 1 Concurrency
Integrations
Amazon S3 Python Tableau Azure Database for PostgreSQL Qlik Sense PowerBI	No integrations available

What are some alternatives to Dremio, 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.