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Amazon Redshift vs Redis: What are the differences?

Amazon Redshift is a cloud-based data warehousing solution optimized for analytics, while Redis is an open-source, in-memory data store used for caching and real-time data processing. Let's explore the key differences between them.

  1. Performance and Scalability: Amazon Redshift is a fully managed petabyte-scale data warehousing service that offers high-performance analytics and supports massive parallel query execution. It is designed for large-scale data applications that require fast and consistent query performance. On the other hand, Redis is an in-memory data structure store that delivers high throughput and low latency. It is primarily used as a caching layer to improve the performance of applications by caching frequently accessed data in memory.

  2. Data Persistence: Amazon Redshift uses a columnar storage format that is optimized for read-heavy workloads. It can store and query large amounts of structured and semi-structured data efficiently. Redis, on the other hand, is an in-memory database that can persist data to disk and provide durability even in the event of system or server failures. It offers different persistence options, including RDB (snapshot-based persistence) and AOF (append-only file persistence).

  3. Data Modeling and Query Language: Amazon Redshift uses a SQL-based querying language that is compatible with most SQL tools and applications. It supports advanced analytical functions and provides features like window functions, distribution styles, and sort keys to optimize query performance. Redis, on the other hand, supports a simple key-value data model and provides a set of commands for data manipulation. It does not have built-in support for complex SQL queries.

  4. Data Replication and High Availability: Amazon Redshift offers automatic replication and backup capabilities to ensure data durability and availability. It uses a replication model with multiple copies of data stored on different nodes for redundancy. Redis, on the other hand, provides replication and high availability through its Redis Sentinel and Redis Cluster features. These features allow data to be replicated across multiple Redis instances and provide failover capabilities in case of node failures.

  5. Data Integration and Ecosystem Integration: Amazon Redshift can easily integrate with other AWS services like Amazon S3, AWS Glue, and AWS Lambda for data ingestion, transformation, and analytics. It also provides connectors for popular BI tools like Tableau and Power BI. Redis, on the other hand, offers a wide range of client libraries and connectors for popular programming languages and frameworks. It can be easily integrated with applications and microservices in various ecosystems.

  6. Data Cost and Pricing Model: Amazon Redshift follows a pay-as-you-go pricing model based on the number of nodes, while also considering factors like data storage and data transfer. It offers different pricing options for on-demand usage and reserved capacity. Redis, on the other hand, can be deployed on various cloud platforms or self-managed on-premises. The cost of Redis deployments depends on factors like cloud provider pricing, instance types, and storage options.

In summary, Amazon Redshift is a data warehousing service optimized for large-scale analytics and offers high-performance query execution, while Redis is an in-memory data store primarily used for caching and delivering high throughput.

Advice on Amazon Redshift and Redis

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.

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Replies (3)
John Nguyen
Recommends
on
AirflowAirflowAWS LambdaAWS Lambda

You could also use AWS Lambda and use Cloudwatch event schedule if you know when the function should be triggered. The benefit is that you could use any language and use the respective database client.

But if you orchestrate ETLs then it makes sense to use Apache Airflow. This requires Python knowledge.

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Recommends
on
AirflowAirflow

Though we have always built something custom, Apache airflow (https://airflow.apache.org/) stood out as a key contender/alternative when it comes to open sources. On the commercial offering, Amazon Redshift combined with Amazon Kinesis (for complex manipulations) is great for BI, though Redshift as such is expensive.

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Recommends

You may want to look into a Data Virtualization product called Conduit. It connects to disparate data sources in AWS, on prem, Azure, GCP, and exposes them as a single unified Spark SQL view to PowerBI (direct query) or Tableau. Allows auto query and caching policies to enhance query speeds and experience. Has a GPU query engine and optimized Spark for fallback. Can be deployed on your AWS VM or on prem, scales up and out. Sounds like the ideal solution to your needs.

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Decisions about Amazon Redshift and Redis
Julien Lafont

Cloud Data-warehouse is the centerpiece of modern Data platform. The choice of the most suitable solution is therefore fundamental.

Our benchmark was conducted over BigQuery and Snowflake. These solutions seem to match our goals but they have very different approaches.

BigQuery is notably the only 100% serverless cloud data-warehouse, which requires absolutely NO maintenance: no re-clustering, no compression, no index optimization, no storage management, no performance management. Snowflake requires to set up (paid) reclustering processes, to manage the performance allocated to each profile, etc. We can also mention Redshift, which we have eliminated because this technology requires even more ops operation.

BigQuery can therefore be set up with almost zero cost of human resources. Its on-demand pricing is particularly adapted to small workloads. 0 cost when the solution is not used, only pay for the query you're running. But quickly the use of slots (with monthly or per-minute commitment) will drastically reduce the cost of use. We've reduced by 10 the cost of our nightly batches by using flex slots.

Finally, a major advantage of BigQuery is its almost perfect integration with Google Cloud Platform services: Cloud functions, Dataflow, Data Studio, etc.

BigQuery is still evolving very quickly. The next milestone, BigQuery Omni, will allow to run queries over data stored in an external Cloud platform (Amazon S3 for example). It will be a major breakthrough in the history of cloud data-warehouses. Omni will compensate a weakness of BigQuery: transferring data in near real time from S3 to BQ is not easy today. It was even simpler to implement via Snowflake's Snowpipe solution.

We also plan to use the Machine Learning features built into BigQuery to accelerate our deployment of Data-Science-based projects. An opportunity only offered by the BigQuery solution

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Pros of Amazon Redshift
Pros of Redis
  • 41
    Data Warehousing
  • 27
    Scalable
  • 17
    SQL
  • 14
    Backed by Amazon
  • 5
    Encryption
  • 1
    Cheap and reliable
  • 1
    Isolation
  • 1
    Best Cloud DW Performance
  • 1
    Fast columnar storage
  • 886
    Performance
  • 542
    Super fast
  • 513
    Ease of use
  • 444
    In-memory cache
  • 324
    Advanced key-value cache
  • 194
    Open source
  • 182
    Easy to deploy
  • 164
    Stable
  • 155
    Free
  • 121
    Fast
  • 42
    High-Performance
  • 40
    High Availability
  • 35
    Data Structures
  • 32
    Very Scalable
  • 24
    Replication
  • 22
    Great community
  • 22
    Pub/Sub
  • 19
    "NoSQL" key-value data store
  • 16
    Hashes
  • 13
    Sets
  • 11
    Sorted Sets
  • 10
    NoSQL
  • 10
    Lists
  • 9
    Async replication
  • 9
    BSD licensed
  • 8
    Bitmaps
  • 8
    Integrates super easy with Sidekiq for Rails background
  • 7
    Keys with a limited time-to-live
  • 7
    Open Source
  • 6
    Lua scripting
  • 6
    Strings
  • 5
    Awesomeness for Free
  • 5
    Hyperloglogs
  • 4
    Transactions
  • 4
    Outstanding performance
  • 4
    Runs server side LUA
  • 4
    LRU eviction of keys
  • 4
    Feature Rich
  • 4
    Written in ANSI C
  • 4
    Networked
  • 3
    Data structure server
  • 3
    Performance & ease of use
  • 2
    Dont save data if no subscribers are found
  • 2
    Automatic failover
  • 2
    Easy to use
  • 2
    Temporarily kept on disk
  • 2
    Scalable
  • 2
    Existing Laravel Integration
  • 2
    Channels concept
  • 2
    Object [key/value] size each 500 MB
  • 2
    Simple

Sign up to add or upvote prosMake informed product decisions

Cons of Amazon Redshift
Cons of Redis
    Be the first to leave a con
    • 15
      Cannot query objects directly
    • 3
      No secondary indexes for non-numeric data types
    • 1
      No WAL

    Sign up to add or upvote consMake informed product decisions

    What is 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.

    What is Redis?

    Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams.

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    Jobs that mention Amazon Redshift and Redis as a desired skillset
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    What are some alternatives to Amazon Redshift and Redis?
    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 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.
    Amazon DynamoDB
    With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.
    Amazon Redshift Spectrum
    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.
    Hadoop
    The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
    See all alternatives