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  1. Stackups
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  5. Amazon Redshift vs IBM DB2

Amazon Redshift vs IBM DB2

OverviewDecisionsComparisonAlternatives

Overview

Amazon Redshift
Amazon Redshift
Stacks1.5K
Followers1.4K
Votes108
IBM DB2
IBM DB2
Stacks245
Followers254
Votes19

Amazon Redshift vs IBM DB2: What are the differences?

Introduction

Amazon Redshift and IBM DB2 are both popular data warehouse solutions used by businesses for analyzing and processing large amounts of data. While they share some similarities, there are key differences between the two platforms that set them apart. In this article, we will explore six specific differences between Amazon Redshift and IBM DB2.

  1. Scalability: One major difference between Amazon Redshift and IBM DB2 is their scalability. Amazon Redshift is built on an MPP (Massively Parallel Processing) architecture, which allows it to scale horizontally by adding more nodes to handle larger workloads. In contrast, IBM DB2 traditionally relied on vertical scaling, where additional resources are added to a single node to increase its capacity. This difference in scalability allows Amazon Redshift to handle larger datasets and query workloads more efficiently.

  2. Cost structure: Another significant difference between Amazon Redshift and IBM DB2 is their cost structure. Amazon Redshift follows a pay-as-you-go pricing model, where customers only pay for the resources they use on an hourly basis. This provides flexibility and cost-efficiency, especially for businesses with fluctuating workloads. On the other hand, IBM DB2 typically involves upfront licensing costs along with ongoing maintenance fees. This makes Amazon Redshift a more attractive option for organizations seeking a cost-effective solution.

  3. Data loading and ingestion: When it comes to data loading and ingestion, Amazon Redshift and IBM DB2 offer different approaches. Amazon Redshift is optimized for bulk data ingestion and processing, making it ideal for scenarios where large datasets need to be loaded quickly. It supports various data loading methods, such as COPY command, bulk INSERT, and data import/export through Amazon S3. IBM DB2, on the other hand, focuses more on transactional processing and supports real-time data ingestion. It provides features like Change Data Capture (CDC) and data replication for seamless updates to the database.

  4. Integration with other services: Amazon Redshift and IBM DB2 differ in their integration capabilities with other services. Amazon Redshift benefits from tight integration with other Amazon Web Services (AWS) offerings, such as Amazon S3, AWS Glue, and AWS Data Pipeline. This enables seamless data movement and integration between different AWS services. In contrast, IBM DB2 offers integration with various IBM software solutions and tools, creating a more comprehensive ecosystem for IBM users. The choice between the two platforms may depend on the specific integration requirements of a business.

  5. Query optimization: Both Amazon Redshift and IBM DB2 employ query optimization techniques to improve performance. However, Amazon Redshift is specifically tailored for analytical workloads and provides advanced optimizations for complex queries involving large datasets. It utilizes columnar storage and massively parallel execution to achieve high query performance. IBM DB2, on the other hand, focuses on a broader range of workloads, including transactional processing, and provides optimizations for different access patterns. The choice of platform should match the workload requirements to achieve optimal query performance.

  6. Ecosystem and community support: Finally, the ecosystem and community support around Amazon Redshift and IBM DB2 differ significantly. Amazon Redshift benefits from the extensive AWS ecosystem, which includes a wide range of services, documentation, and community resources. This contributes to a robust and active user community that can provide support and guidance. IBM DB2 has its own ecosystem and community support, with a focus on the IBM software stack and a more specialized user base. The availability of resources and community support can play a significant role in the ease of adoption and maintenance of the chosen data warehouse platform.

In Summary, Amazon Redshift and IBM DB2 differ in terms of scalability, cost structure, data loading and ingestion capabilities, integration with other services, query optimization techniques, and ecosystem/community support. These differences make them suitable for different use cases and should be considered when choosing the right data warehouse solution for specific business needs.

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Advice on Amazon Redshift, IBM DB2

datocrats-org
datocrats-org

Jul 29, 2020

Needs adviceonAmazon EC2Amazon EC2TableauTableauPowerBIPowerBI

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
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
Amazon Redshift
IBM DB2
IBM DB2

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.

DB2 for Linux, UNIX, and Windows is optimized to deliver industry-leading performance across multiple workloads, while lowering administration, storage, development, and server costs.

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>
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Statistics
Stacks
1.5K
Stacks
245
Followers
1.4K
Followers
254
Votes
108
Votes
19
Pros & Cons
Pros
  • 41
    Data Warehousing
  • 27
    Scalable
  • 17
    SQL
  • 14
    Backed by Amazon
  • 5
    Encryption
Pros
  • 7
    Rock solid and very scalable
  • 5
    BLU Analytics is amazingly fast
  • 2
    Secure by default
  • 2
    Easy
  • 2
    Native XML support
Integrations
SQLite
SQLite
MySQL
MySQL
Oracle PL/SQL
Oracle PL/SQL
Node.js
Node.js
JavaScript
JavaScript
PHP
PHP
Ruby
Ruby
Java
Java
Python
Python
C#
C#
.NET
.NET
C++
C++
Perl
Perl

What are some alternatives to Amazon Redshift, IBM DB2?

MongoDB

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

MySQL

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

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