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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.
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.
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).
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.
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.
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.
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.
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.
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.
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.
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.
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
Pros of Amazon Redshift
- Data Warehousing41
- Scalable27
- SQL17
- Backed by Amazon14
- Encryption5
- Cheap and reliable1
- Isolation1
- Best Cloud DW Performance1
- Fast columnar storage1
Pros of Redis
- Performance886
- Super fast542
- Ease of use513
- In-memory cache444
- Advanced key-value cache324
- Open source194
- Easy to deploy182
- Stable164
- Free155
- Fast121
- High-Performance42
- High Availability40
- Data Structures35
- Very Scalable32
- Replication24
- Great community22
- Pub/Sub22
- "NoSQL" key-value data store19
- Hashes16
- Sets13
- Sorted Sets11
- NoSQL10
- Lists10
- Async replication9
- BSD licensed9
- Bitmaps8
- Integrates super easy with Sidekiq for Rails background8
- Keys with a limited time-to-live7
- Open Source7
- Lua scripting6
- Strings6
- Awesomeness for Free5
- Hyperloglogs5
- Transactions4
- Outstanding performance4
- Runs server side LUA4
- LRU eviction of keys4
- Feature Rich4
- Written in ANSI C4
- Networked4
- Data structure server3
- Performance & ease of use3
- Dont save data if no subscribers are found2
- Automatic failover2
- Easy to use2
- Temporarily kept on disk2
- Scalable2
- Existing Laravel Integration2
- Channels concept2
- Object [key/value] size each 500 MB2
- Simple2
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Cons of Amazon Redshift
Cons of Redis
- Cannot query objects directly15
- No secondary indexes for non-numeric data types3
- No WAL1