Overview

Amazon Redshift Spectrum

Stacks99

Followers147

Votes3

Dremio

Stacks116

Followers348

Votes8

Amazon Redshift Spectrum vs Dremio: What are the differences?

# Introduction: 
Amazon Redshift Spectrum and Dremio are both data analytics solutions that enable users to query and analyze data. However, there are key differences between the two platforms that distinguish them in terms of performance, compatibility, and ease of use.

1. **Scalability and Performance**: Amazon Redshift Spectrum is designed to handle large-scale data processing, enabling users to query data stored in Amazon S3 without needing to load it into Redshift. On the other hand, Dremio's architecture allows for faster and more efficient query processing by leveraging capabilities such as in-memory columnar execution and query acceleration.

2. **Data Storage Integration**: Redshift Spectrum is tightly integrated with Amazon S3, providing seamless access to data stored in S3 for querying and analysis. In contrast, while Dremio supports various storage systems including HDFS, S3, and more, it offers a more agnostic approach to data storage integration, enabling users to access and query data across different storage systems without vendor lock-in.

3. **Ease of Use**: Amazon Redshift Spectrum offers a managed service that simplifies the setup and maintenance of data processing tasks. Dremio, on the other hand, provides a self-service data platform that enables users to easily connect, analyze, and visualize data from different sources with minimal setup and configuration.

4. **Cost**: Redshift Spectrum pricing is based on the amount of data scanned during query execution, while Dremio offers a subscription-based pricing model that includes enterprise support and access to additional features. Depending on the specific use case and data workload, one platform may be more cost-effective than the other.

5. **Query Optimization and Acceleration**: Dremio emphasizes query optimization and acceleration techniques such as query planning, data curation, and adaptive execution, aiming to provide users with faster query performance and more efficient data processing. Redshift Spectrum also offers query optimization capabilities, but the focus is more on scalability and data processing on a larger scale.

6. **Integration with BI Tools**: Dremio provides seamless integration with popular business intelligence tools such as Tableau, Power BI, and Looker, allowing users to analyze and visualize data directly from these tools with minimal setup. While Redshift Spectrum can also integrate with BI tools, the level of integration and ease of use may vary compared to Dremio's integration capabilities.

In Summary, Amazon Redshift Spectrum and Dremio differ in terms of scalability and performance, data storage integration, ease of use, cost, query optimization, and integration with BI tools.

🔥 Trending in Databases on StackShare

PostgreSQL Databases

A powerful, open source object-relational database system

Try it View Docs Alternatives

Try

431

83.9k

Advice on Amazon Redshift Spectrum, Dremio

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

Amazon Redshift Spectrum	Dremio
With Redshift Spectrum, you can extend the analytic power of Amazon Redshift beyond data stored on local disks in your data warehouse to query vast amounts of unstructured data in your Amazon S3 “data lake” -- without having to load or transform any data.	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.
-	Democratize all your data; Make your data engineers more productive; Accelerate your favorite tools; Self service, for everybody
Statistics
Stacks 99	Stacks 116
Followers 147	Followers 348
Votes 3	Votes 8
Pros & Cons
Pros 1 Economical 1 Great Documentation 1 Good Performance	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
Integrations
Amazon S3 Amazon Redshift	Amazon S3 Python Tableau Azure Database for PostgreSQL Qlik Sense PowerBI

What are some alternatives to Amazon Redshift Spectrum, Dremio?

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.

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.

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.