Amazon Redshift vs pgweb

Overview

Amazon Redshift

Stacks1.5K

Followers1.4K

Votes108

pgweb

Stacks8

Followers16

Votes0

GitHub Stars9.1K

Forks803

Amazon Redshift vs pgweb: What are the differences?

## Introduction
In this comparison, we will highlight key differences between Amazon Redshift and pgweb.

1. **Performance**: Amazon Redshift is a fully managed, high-performance data warehouse service, optimized for online analytical processing (OLAP) workloads, whereas pgweb is a web-based database browser for PostgreSQL databases without specific optimizations for data warehousing.
   
2. **Scalability**: Amazon Redshift is designed to scale effortlessly by adding additional nodes to the cluster, allowing for increased storage and compute power on-demand. Pgweb, on the other hand, lacks built-in scalability features as it mainly serves as a tool for database management and query execution.
   
3. **Pricing Model**: Amazon Redshift follows a pay-as-you-go pricing model based on the type and number of nodes used, along with the amount of data stored. Pgweb is an open-source tool and does not have any associated costs for usage.
   
4. **Data Backup and Recovery**: Amazon Redshift provides automated backups and point-in-time recovery to ensure data durability and reliability. Pgweb does not offer out-of-the-box data backup and recovery options and relies on external methods for these functionalities.
   
5. **Built-in Features**: Amazon Redshift includes various advanced features like data compression, columnar storage, and parallel query execution to optimize performance and efficiency. Pgweb, being a lightweight web tool, focuses more on providing a user-friendly interface for database interactions without advanced data warehousing features.
   
6. **Support and Documentation**: Amazon Redshift offers comprehensive support and documentation from Amazon Web Services (AWS) for troubleshooting, optimization, and best practices. Pgweb, being an open-source project, relies on community support and forums for assistance and may not have as extensive documentation and professional support services.

In Summary, Amazon Redshift and pgweb differ significantly in terms of performance optimization, scalability options, pricing model, data backup capabilities, built-in features, and support resources.

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Advice on Amazon Redshift, pgweb

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

Julien

CTO at Hawk

Sep 19, 2020

Decided

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

193k views193k

Comments

Detailed Comparison

Amazon Redshift	pgweb
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.	This is a web-based browser for PostgreSQL database server. Its written in Go and works on Mac OSX, Linux and Windows machines. Main idea behind using Go for the backend is to utilize language's ability for cross-compile source code for multiple platforms. This project is an attempt to create a very simple and portable application to work with PostgreSQL databases.
Optimized for Data Warehousing- It uses columnar storage, data compression, and zone maps to reduce the amount of IO needed to perform queries. Redshift has a massively parallel processing (MPP) architecture, parallelizing and distributing SQL operations to take advantage of all available resources.;Scalable- With a few clicks of the AWS Management Console or a simple API call, you can easily scale the number of nodes in your data warehouse up or down as your performance or capacity needs change.;No Up-Front Costs- You pay only for the resources you provision. You can choose On-Demand pricing with no up-front costs or long-term commitments, or obtain significantly discounted rates with Reserved Instance pricing.;Fault Tolerant- Amazon Redshift has multiple features that enhance the reliability of your data warehouse cluster. All data written to a node in your cluster is automatically replicated to other nodes within the cluster and all data is continuously backed up to Amazon S3.;SQL - Amazon Redshift is a SQL data warehouse and uses industry standard ODBC and JDBC connections and Postgres drivers.;Isolation - Amazon Redshift enables you to configure firewall rules to control network access to your data warehouse cluster.;Encryption – With just a couple of parameter settings, you can set up Amazon Redshift to use SSL to secure data in transit and hardware-acccelerated AES-256 encryption for data at rest.<br>	Connect to local or remote server;Browse tables and table rows;Get table details: structure, size, indices, row count;Execute SQL query and run analyze on it;Export query results to CSV;View query history
Statistics
GitHub Stars -	GitHub Stars 9.1K
GitHub Forks -	GitHub Forks 803
Stacks 1.5K	Stacks 8
Followers 1.4K	Followers 16
Votes 108	Votes 0
Pros & Cons
Pros 41 Data Warehousing 27 Scalable 17 SQL 14 Backed by Amazon 5 Encryption	No community feedback yet
Integrations
SQLite MySQL Oracle PL/SQL	PostgreSQL

What are some alternatives to Amazon Redshift, pgweb?

dbForge Studio for MySQL

It is the universal MySQL and MariaDB client for database management, administration and development. With the help of this intelligent MySQL client the work with data and code has become easier and more convenient. This tool provides utilities to compare, synchronize, and backup MySQL databases with scheduling, and gives possibility to analyze and report MySQL tables data.

dbForge Studio for Oracle

It is a powerful integrated development environment (IDE) which helps Oracle SQL developers to increase PL/SQL coding speed, provides versatile data editing tools for managing in-database and external data.

dbForge Studio for PostgreSQL

It is a GUI tool for database development and management. The IDE for PostgreSQL allows users to create, develop, and execute queries, edit and adjust the code to their requirements in a convenient and user-friendly interface.

dbForge Studio for SQL Server

It is a powerful IDE for SQL Server management, administration, development, data reporting and analysis. The tool will help SQL developers to manage databases, version-control database changes in popular source control systems, speed up routine tasks, as well, as to make complex database changes.

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.

Liquibase

Liquibase is th leading open-source tool for database schema change management. Liquibase helps teams track, version, and deploy database schema and logic changes so they can automate their database code process with their app code process.

Sequel Pro

Sequel Pro is a fast, easy-to-use Mac database management application for working with MySQL databases.

DBeaver

It is a free multi-platform database tool for developers, SQL programmers, database administrators and analysts. Supports all popular databases: MySQL, PostgreSQL, SQLite, Oracle, DB2, SQL Server, Sybase, Teradata, MongoDB, Cassandra, Redis, etc.

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

dbForge SQL Complete

It is an IntelliSense add-in for SQL Server Management Studio, designed to provide the fastest T-SQL query typing ever possible.

Related Comparisons

Amazon Redshift vs pgweb: What are the differences?

## Introduction
In this comparison, we will highlight key differences between Amazon Redshift and pgweb.

1. **Performance**: Amazon Redshift is a fully managed, high-performance data warehouse service, optimized for online analytical processing (OLAP) workloads, whereas pgweb is a web-based database browser for PostgreSQL databases without specific optimizations for data warehousing.
   
2. **Scalability**: Amazon Redshift is designed to scale effortlessly by adding additional nodes to the cluster, allowing for increased storage and compute power on-demand. Pgweb, on the other hand, lacks built-in scalability features as it mainly serves as a tool for database management and query execution.
   
3. **Pricing Model**: Amazon Redshift follows a pay-as-you-go pricing model based on the type and number of nodes used, along with the amount of data stored. Pgweb is an open-source tool and does not have any associated costs for usage.
   
4. **Data Backup and Recovery**: Amazon Redshift provides automated backups and point-in-time recovery to ensure data durability and reliability. Pgweb does not offer out-of-the-box data backup and recovery options and relies on external methods for these functionalities.
   
5. **Built-in Features**: Amazon Redshift includes various advanced features like data compression, columnar storage, and parallel query execution to optimize performance and efficiency. Pgweb, being a lightweight web tool, focuses more on providing a user-friendly interface for database interactions without advanced data warehousing features.
   
6. **Support and Documentation**: Amazon Redshift offers comprehensive support and documentation from Amazon Web Services (AWS) for troubleshooting, optimization, and best practices. Pgweb, being an open-source project, relies on community support and forums for assistance and may not have as extensive documentation and professional support services.

Amazon Redshift vs pgweb

Overview