Overview

IBM DB2

Stacks245

Followers254

Votes19

Hadoop

Stacks2.7K

Followers2.3K

Votes56

GitHub Stars15.3K

Forks9.1K

Hadoop vs IBM DB2: What are the differences?

Introduction

Hadoop and IBM DB2 are both popular technologies used in the field of data storage and processing, but they have several key differences that set them apart from each other.

Architecture: Hadoop is an open-source framework that utilizes a distributed file system and a MapReduce processing model to handle big data. On the other hand, IBM DB2 is a proprietary, relational database management system (RDBMS) that follows a traditional centralized architecture.
Data Processing: Hadoop is designed to handle unstructured and semi-structured data efficiently. It provides a scalable and fault-tolerant platform for processing large volumes of data in parallel. In contrast, IBM DB2 is optimized for structured data processing and offers various relational database management features, such as indexing, querying, and transaction handling.
Scalability: Hadoop is highly scalable and can easily handle petabytes of data by adding more commodity hardware to the cluster. It provides a distributed computing environment, allowing data processing to be spread across multiple nodes. In comparison, IBM DB2's scalability is limited by the capacity of a single server instance, making it more suitable for smaller to medium-sized datasets.
Data Storage: Hadoop uses a distributed file system called HDFS (Hadoop Distributed File System) for storing data across multiple machines in a cluster. This enables fault tolerance and high availability. In contrast, IBM DB2 stores data in a structured manner using tables, indexes, and other database objects within a single server instance.
Processing Speed: Hadoop excels at processing large volumes of data by distributing the workload across a cluster of machines. It can leverage parallel processing and perform computations in a distributed manner, leading to faster processing times for big data tasks. IBM DB2, being a traditional RDBMS, is optimized for transaction processing and handling structured data efficiently.
Cost: Hadoop is an open-source framework and allows organizations to utilize commodity hardware, resulting in a lower total cost of ownership. Conversely, IBM DB2 is a proprietary technology and typically involves licensing costs, making it comparatively more expensive.

In summary, Hadoop is an open-source, distributed framework suited for processing big data with its scalability, fault tolerance, and parallel processing capabilities. In contrast, IBM DB2 is a proprietary relational database management system optimized for structured data processing and transaction handling.

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Advice on IBM DB2, Hadoop

Mr

SVP CTO

Apr 22, 2021

Needs adviceon

MarkLogic

Hadoop

Snowflake

For a property and casualty insurance company, we currently use MarkLogic and Hadoop for our raw data lake. Trying to figure out how snowflake fits in the picture. Does anybody have some good suggestions/best practices for when to use and what data to store in Mark logic versus Snowflake versus a hadoop or all three of these platforms redundant with one another?

136k views136k

Comments

Mr

SVP CTO

Apr 22, 2021

Needs advice

for property and casualty insurance company we current Use marklogic and Hadoop for our raw data lake. Trying to figure out how snowflake fits in the picture. Does anybody have some good suggestions/best practices for when to use and what data to store in Mark logic versus snowflake versus a hadoop or all three of these platforms redundant with one another?

23.6k views23.6k

Comments

pionell

Sep 16, 2020

Needs adviceon

MariaDB

I have a lot of data that's currently sitting in a MariaDB database, a lot of tables that weigh 200gb with indexes. Most of the large tables have a date column which is always filtered, but there are usually 4-6 additional columns that are filtered and used for statistics. I'm trying to figure out the best tool for storing and analyzing large amounts of data. Preferably self-hosted or a cheap solution. The current problem I'm running into is speed. Even with pretty good indexes, if I'm trying to load a large dataset, it's pretty slow.

159k views159k

Comments

Detailed Comparison

IBM DB2	Hadoop
DB2 for Linux, UNIX, and Windows is optimized to deliver industry-leading performance across multiple workloads, while lowering administration, storage, development, and server costs.	The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
Statistics
GitHub Stars -	GitHub Stars 15.3K
GitHub Forks -	GitHub Forks 9.1K
Stacks 245	Stacks 2.7K
Followers 254	Followers 2.3K
Votes 19	Votes 56
Pros & Cons
Pros 7 Rock solid and very scalable 5 BLU Analytics is amazingly fast 2 Native XML support 2 Secure by default 2 Easy	Pros 39 Great ecosystem 11 One stack to rule them all 4 Great load balancer 1 Java syntax 1 Amazon aws
Integrations
Node.js JavaScript PHP Ruby Java Python C# .NET C++ Perl	No integrations available

What are some alternatives to IBM DB2, Hadoop?

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

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

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

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 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

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 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

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.