Amazon Redshift vs MemSQL

Overview

Amazon Redshift

Stacks1.5K

Followers1.4K

Votes108

MemSQL

Stacks86

Followers184

Votes44

Amazon Redshift vs MemSQL: What are the differences?

Key Differences between Amazon Redshift and MemSQL

Data Storage and Processing: Amazon Redshift is a fully-managed data warehouse service that uses columnar storage and massively parallel processing for fast querying and analysis of large data sets. In contrast, MemSQL is an in-memory, distributed database that leverages both row-based and columnar storage for high-speed data processing. Redshift focuses on optimized storage and query performance, while MemSQL prioritizes real-time analytics and transactional processing.
Scale and Performance: Redshift can handle petabytes of data and scale up to thousands of nodes for parallel processing, enabling it to handle large workloads. MemSQL is designed for horizontal scaling and can distribute data across multiple nodes to achieve high performance and scalability. While Redshift is optimized for large-scale data warehousing, MemSQL excels in fast real-time and analytical applications that require high concurrency.
Data Ingestion and Integration: Redshift supports various data ingestion methods, including bulk loading, streaming, and data transfer services like Amazon Kinesis. MemSQL also offers multiple data ingestion options, including bulk loading, change data capture, and real-time streaming through connectors like Apache Kafka. Both platforms provide integrations with popular data integration tools, making it easy to load and process data from different sources.
SQL Compatibility: Redshift is based on PostgreSQL and supports a large subset of ANSI SQL standards. It also provides extensions specific to data warehousing, such as window functions and support for advanced analytics. MemSQL is SQL-compliant but offers additional features and specialized functions for distributed processing and real-time analytics. It also supports the MySQL wire protocol, enabling easy migration from MySQL-based applications.
Data Replication and High Availability: Redshift offers automated backups, snapshots, and replication across multiple availability zones for data durability and disaster recovery. It also supports cross-region replication for global data availability. MemSQL provides built-in replication and high availability features by replicating data across multiple nodes in a cluster, ensuring durability and fault tolerance. It also offers automated backups and supports replication to secondary MemSQL clusters.
Pricing Model: Redshift follows a pay-per-usage pricing model, where users pay for the compute nodes and storage used. It offers different pricing tiers based on the node type and usage requirements. MemSQL has a subscription-based pricing model with options for both on-premises and cloud deployments. The pricing is based on the number of cores and the amount of RAM used by the cluster.

In Summary, Amazon Redshift and MemSQL differ in their data storage and processing approaches, scale and performance capabilities, data ingestion and integration methods, SQL compatibility, data replication and high availability features, and pricing models.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs

CLI (Node.js)

Manual

Advice on Amazon Redshift, MemSQL

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	MemSQL
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.	MemSQL converges transactions and analytics for sub-second data processing and reporting. Real-time businesses can build robust applications on a simple and scalable infrastructure that complements and extends existing data pipelines.
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>	ANSI SQL Support;Fully-distributed Joins;Compiled Queries; ACID Compliance;In-Memory Tables;On-Disk Tables; Massively Parallel Execution;Lock Free Data Structures;JSON Support; High Availability; Online Backup and Restore;Online Replication
Statistics
Stacks 1.5K	Stacks 86
Followers 1.4K	Followers 184
Votes 108	Votes 44
Pros & Cons
Pros 41 Data Warehousing 27 Scalable 17 SQL 14 Backed by Amazon 5 Encryption	Pros 9 Distributed 5 Realtime 4 Sql 4 Concurrent 4 Columnstore
Integrations
SQLite MySQL Oracle PL/SQL	Google Compute Engine MySQL QlikView

What are some alternatives to Amazon Redshift, MemSQL?

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.

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.

Qubole

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

Hazelcast

With its various distributed data structures, distributed caching capabilities, elastic nature, memcache support, integration with Spring and Hibernate and more importantly with so many happy users, Hazelcast is feature-rich, enterprise-ready and developer-friendly in-memory data grid solution.

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

Aerospike

Aerospike is an open-source, modern database built from the ground up to push the limits of flash storage, processors and networks. It was designed to operate with predictable low latency at high throughput with uncompromising reliability – both high availability and ACID guarantees.

Apache Ignite

It is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads delivering in-memory speeds at petabyte scale

Altiscale

we run Apache Hadoop for you. We not only deploy Hadoop, we monitor, manage, fix, and update it for you. Then we take it a step further: We monitor your jobs, notify you when something’s wrong with them, and can help with tuning.

Snowflake

Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.

SAP HANA

It is an application that uses in-memory database technology that allows the processing of massive amounts of real-time data in a short time. The in-memory computing engine allows it to process data stored in RAM as opposed to reading it from a disk.

Related Comparisons

Amazon Redshift vs MemSQL: What are the differences?

Key Differences between Amazon Redshift and MemSQL

Data Storage and Processing: Amazon Redshift is a fully-managed data warehouse service that uses columnar storage and massively parallel processing for fast querying and analysis of large data sets. In contrast, MemSQL is an in-memory, distributed database that leverages both row-based and columnar storage for high-speed data processing. Redshift focuses on optimized storage and query performance, while MemSQL prioritizes real-time analytics and transactional processing.
Scale and Performance: Redshift can handle petabytes of data and scale up to thousands of nodes for parallel processing, enabling it to handle large workloads. MemSQL is designed for horizontal scaling and can distribute data across multiple nodes to achieve high performance and scalability. While Redshift is optimized for large-scale data warehousing, MemSQL excels in fast real-time and analytical applications that require high concurrency.
Data Ingestion and Integration: Redshift supports various data ingestion methods, including bulk loading, streaming, and data transfer services like Amazon Kinesis. MemSQL also offers multiple data ingestion options, including bulk loading, change data capture, and real-time streaming through connectors like Apache Kafka. Both platforms provide integrations with popular data integration tools, making it easy to load and process data from different sources.
SQL Compatibility: Redshift is based on PostgreSQL and supports a large subset of ANSI SQL standards. It also provides extensions specific to data warehousing, such as window functions and support for advanced analytics. MemSQL is SQL-compliant but offers additional features and specialized functions for distributed processing and real-time analytics. It also supports the MySQL wire protocol, enabling easy migration from MySQL-based applications.
Data Replication and High Availability: Redshift offers automated backups, snapshots, and replication across multiple availability zones for data durability and disaster recovery. It also supports cross-region replication for global data availability. MemSQL provides built-in replication and high availability features by replicating data across multiple nodes in a cluster, ensuring durability and fault tolerance. It also offers automated backups and supports replication to secondary MemSQL clusters.
Pricing Model: Redshift follows a pay-per-usage pricing model, where users pay for the compute nodes and storage used. It offers different pricing tiers based on the node type and usage requirements. MemSQL has a subscription-based pricing model with options for both on-premises and cloud deployments. The pricing is based on the number of cores and the amount of RAM used by the cluster.

Amazon Redshift vs MemSQL

Overview

Amazon Redshift vs MemSQL: What are the differences?