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Amazon Redshift vs Sequel Pro: What are the differences?
Introduction
Amazon Redshift and Sequel Pro are both tools used for managing databases, but they have several key differences. Understanding these differences can help determine which tool is best suited for different use cases.
Data Warehouse vs. Database Management: Amazon Redshift is a fully-managed data warehousing service provided by Amazon Web Services (AWS), whereas Sequel Pro is a desktop application used for managing databases, focused on MySQL databases specifically. Redshift is designed to handle large volumes of structured and semi-structured data and is optimized for online analytical processing (OLAP), while Sequel Pro is more oriented towards database administration and development tasks.
Scalability and Performance: Redshift is highly scalable and can handle petabytes of data with ease. It uses a clustered columnar storage approach and distributed computing to provide fast query performance. On the other hand, Sequel Pro is a client-based application and its performance is limited by the resources of the local machine running the software. It may struggle to handle larger datasets and complex queries compared to Redshift.
Pricing and Cost: As a cloud-based service, Redshift follows a pay-as-you-go pricing model, where users are charged based on the amount of storage used and the number of requested concurrent queries. Sequel Pro, being a desktop application, doesn't have any specific pricing model and can be used without additional costs once installed. However, it should be noted that the hardware and resources required to run Sequel Pro efficiently may have associated costs.
Accessibility and Availability: Redshift is a cloud-based service, meaning it can be accessed from anywhere with an internet connection. It offers high availability and provides automatic backups and fault tolerance. Sequel Pro, being a client-side application, requires local installation and can only be accessed from the machine it is installed on. It relies on the availability of the local machine and does not provide built-in backup and fault tolerance mechanisms.
Advanced Analytics and Data Processing: Redshift provides advanced analytics capabilities through integration with other AWS services like Amazon Machine Learning and Amazon QuickSight. It also supports complex data processing and transformations through SQL and various data loading methods. Sequel Pro, being a database management tool, focuses more on traditional database operations such as querying, creating and managing tables, and running ad-hoc SQL commands.
Community and Support: Redshift benefits from being an AWS service, which comes with a large and active community. It has extensive documentation, user forums, and access to AWS support services. Sequel Pro, while popular among MySQL users, does not have the same level of community support and official documentation. However, being an open-source project, it benefits from community contributions and has its own support channels.
In summary, Amazon Redshift is a cloud-based data warehousing service optimized for large-scale data analysis and advanced analytics. Sequel Pro, on the other hand, is a desktop application focused on MySQL database management and development tasks. Each tool has its own strengths and should be chosen based on the specific requirements of the project at hand.
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 Sequel Pro
- Free25
- Simple18
- Clean UI17
- Easy8
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Cons of Amazon Redshift
Cons of Sequel Pro
- Only available for Mac OS1
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