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Databricks Unified Analytics Platform VS Amazon Redshift

Compare Databricks Unified Analytics Platform VS Amazon Redshift and see what are their differences

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Databricks Unified Analytics Platform logo Databricks Unified Analytics Platform

One platform for accelerating data-driven innovation across data engineering, data science & business analytics

Amazon Redshift logo Amazon Redshift

Learn about Amazon Redshift cloud data warehouse.
  • Databricks Unified Analytics Platform Landing page
    Landing page //
    2023-07-11
  • Amazon Redshift Landing page
    Landing page //
    2023-03-14

Databricks Unified Analytics Platform features and specs

  • Scalability
    Databricks is built on Apache Spark, which allows for easy scaling of data processing and analytics operations across large datasets.
  • Integrated Environment
    Provides a unified analytics platform that combines data engineering, data science, and data warehouse capabilities, simplifying workflows.
  • Collaborative Workspace
    Enables collaboration between data engineers, data scientists, and analysts with its interactive notebooks and real-time collaboration features.
  • Lakehouse Architecture
    Combines the best features of data lakes and data warehouses, providing structured transactional data access over unstructured data.
  • Support for Multiple Languages
    Offers support for multiple programming languages such as Python, R, SQL, and Scala, making it versatile for different users.

Possible disadvantages of Databricks Unified Analytics Platform

  • Complexity
    Despite its powerful features, the platform can be complex to set up and manage, particularly for teams unfamiliar with similar environments.
  • Cost
    The platform can become expensive, especially when scaling operations and running large workloads continuously.
  • Learning Curve
    New users might face a steep learning curve, requiring training and practice to use the platform effectively.
  • Vendor Lock-In
    Using proprietary tools and integrations could lead to dependency on Databricks, making it harder to switch to other solutions in the future.
  • Limited Offline Features
    As a cloud-native platform, Databricks relies heavily on internet connectivity, lacking robust offline features for some use cases.

Amazon Redshift features and specs

  • Scalability
    Amazon Redshift allows you to scale your data warehouse up or down easily based on your needs with just a few clicks or by using the API, providing flexibility to handle varying workloads.
  • Performance
    Redshift uses columnar storage, parallel processing, and efficient data compression techniques to deliver high performance for complex queries and large datasets.
  • Integration
    It seamlessly integrates with various AWS services like S3, DynamoDB, and QuickSight, making it easier to build a comprehensive data ecosystem.
  • Cost-effective
    Redshift offers a pay-as-you-go pricing model with no upfront costs, and you can save more with reserved instances, making it cost-effective for many businesses.
  • Security
    It includes features like encryption, Virtual Private Cloud (VPC), and compliance certifications (such as SOC 1, SOC 2, SOC 3, and more) to ensure data security and compliance.
  • Managed Service
    Amazon Redshift is a fully managed service, so it takes care of managing, monitoring, and scaling the infrastructure, allowing you to focus on your data and insights.

Possible disadvantages of Amazon Redshift

  • Complexity
    Although Redshift is powerful, it can be complex to set up, configure, and optimize for best performance, requiring knowledge and experience in data warehousing.
  • Cost for Unused Resources
    While Redshift is cost-effective for large-scale operations, costs can add up quickly if resources are not managed properly, especially with long-running clusters that are under-utilized.
  • Maintenance Windows
    Despite being a managed service, maintenance windows and updates can occasionally lead to downtime or performance degradation, impacting availability.
  • Data Transfer Costs
    Transferring data in and out of Redshift can incur additional costs, particularly if large volumes of data are involved, which can affect overall budget planning.
  • Vendor Lock-in
    Using Amazon Redshift ties you to the AWS ecosystem, which could be a disadvantage if you are considering a multi-cloud strategy or planning to switch providers in the future.

Databricks Unified Analytics Platform videos

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Amazon Redshift videos

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Getting Started with Amazon Redshift - AWS Online Tech Talks

More videos:

  • Review - Amazon Redshift Materialized Views
  • Tutorial - Amazon Redshift Tutorial | Amazon Redshift Architecture | AWS Tutorial For Beginners | Simplilearn

Category Popularity

0-100% (relative to Databricks Unified Analytics Platform and Amazon Redshift)

Databricks Unified Analytics Platform

Amazon Redshift

Office & Productivity
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Office & Productivity
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Databases
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Databases
100% 100
Development
100 100%
Development
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Big Data
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Big Data
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User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Databricks Unified Analytics Platform and Amazon Redshift

Databricks Unified Analytics Platform Reviews

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Amazon Redshift Reviews

Data Warehouse Tools
No, SQL (Structured Query Language) is not a data warehouse itself. SQL is a programming language used for managing and querying data stored in relational database management systems (RDBMS) and data warehouses. Many data warehouse solutions, such as Peliqan, Amazon Redshift, and PostgreSQL, support SQL for querying and analyzing data within the data warehouse
Source: peliqan.io
Top 6 Cloud Data Warehouses in 2023
Coined in November 2021, Amazon Redshift was launched as a fully managed cloud data warehouse that can handle petabyte-scale data. While it was not the first cloud data warehouse, it became the first to proliferate in the market share after a large-scale adoption. Redshift uses SQL dialect based on PostgreSQL, which is well-known by many analysts globally, and its...
Source: geekflare.com
Top 5 Cloud Data Warehouses in 2023
Jan 11, 2023 The 5 best cloud data warehouse solutions in 2023Google BigQuerySource: https://cloud.google.com/bigqueryBest for:Top features:Pros:Cons:Pricing:SnowflakeBest for:Top features:Pros:Cons:Pricing:Amazon RedshiftSource: https://aws.amazon.com/redshift/Best for:Top features:Pros:Cons:Pricing:FireboltSource: https://www.firebolt.io/Best for:Top...
Source: www.shipyardapp.com
Top 5 BigQuery Alternatives: A Challenge of Complexity
As the most proven tool in this category, Amazon Redshift is a fully managed cloud-based data warehouse used to collect and store data. Like BigQuery, Redshift seamlessly integrates with multiple products and ETL services.
Source: blog.panoply.io

Social recommendations and mentions

Based on our record, Amazon Redshift seems to be a lot more popular than Databricks Unified Analytics Platform. While we know about 29 links to Amazon Redshift, we've tracked only 1 mention of Databricks Unified Analytics Platform. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Databricks Unified Analytics Platform mentions (1)

  • Should I replicate all our transactional DB to Redshift?
    See more here: https://databricks.com/product/data-lakehouse. Source: about 3 years ago

Amazon Redshift mentions (29)

  • How to Pitch Your Boss to Adopt Apache Iceberg?
    If your team is managing large volumes of historical data using platforms like Snowflake, Amazon Redshift, or Google BigQuery, you’ve probably noticed a shift happening in the data engineering world. A new generation of data infrastructure is forming — one that prioritizes openness, interoperability, and cost-efficiency. At the center of that shift is Apache Iceberg. - Source: dev.to / about 1 month ago
  • Everyone Uses Postgres… But Why?
    Postgres can be easily adapted to build highly tailored solutions. For instance, Amazon Redshift can be considered a highly scalable fork of Postgres. It’s a distributed database focusing on OLAP workloads that you can deploy in AWS. - Source: dev.to / 6 months ago
  • From ETL and ELT to Reverse ETL
    With the transition from ETL to ELT, data warehouses have ascended to the role of data custodians, centralizing customer data collected from fragmented systems. This pivotal shift has been enabled by a suite of powerful tools: Fivetran and Airbyte streamline the extraction and loading, DBT handles the transformation, and robust warehousing solutions like Snowflake and Redshift store the data. While traditionally... - Source: dev.to / 7 months ago
  • Choosing Between a Streaming Database and a Stream Processing Framework in Python
    They differ from conventional analytic databases like Snowflake, Redshift, BigQuery, and Oracle in several ways. Conventional databases are batch-oriented, loading data in defined windows like hourly, daily, weekly, and so on. While loading data, conventional databases lock the tables, making the newly loaded data unavailable until the batch load is fully completed. Streaming databases continuously receive new... - Source: dev.to / over 1 year ago
  • Choosing the Right AWS Database: A Guide for Modern Applications
    Data warehousing is the process of storing and analyzing large volumes of data for business intelligence and analytics purposes. AWS offers a fully managed data warehousing service called Amazon Redshift that can handle petabyte-scale data warehouses with ease. - Source: dev.to / over 1 year ago
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What are some alternatives?

When comparing Databricks Unified Analytics Platform and Amazon Redshift, you can also consider the following products

Saturn Cloud - ML in the cloud. Loved by Data Scientists, Control for IT. Advance your business's ML capabilities through the entire experiment tracking lifecycle. Available on multiple clouds: AWS, Azure, GCP, and OCI.

Google BigQuery - A fully managed data warehouse for large-scale data analytics.

Amazon SageMaker - Amazon SageMaker provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly.

Microsoft SQL Server - Microsoft Azure is an open, flexible, enterprise-grade cloud computing platform. Move faster, do more, and save money with IaaS + PaaS. Try for FREE.

Azure Synapse Analytics - Get started with Azure SQL Data Warehouse for an enterprise-class SQL Server experience. Cloud data warehouses offer flexibility, scalability, and big data insights.

Vertica - Vertica is a grid-based, column-oriented database designed to manage large, fast-growing volumes of...

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