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  1. Stackups
  2. Application & Data
  3. Databases
  4. Big Data As A Service
  5. Google BigQuery vs IBM DB2

Google BigQuery vs IBM DB2

OverviewComparisonAlternatives

Overview

Google BigQuery
Google BigQuery
Stacks1.8K
Followers1.5K
Votes152
IBM DB2
IBM DB2
Stacks245
Followers254
Votes19

Google BigQuery vs IBM DB2: What are the differences?

Introduction

Google BigQuery and IBM DB2 are two popular database management systems that offer different features and functionalities. In this comparison, we will highlight the key differences between these two platforms.

  1. Pricing Model: Google BigQuery offers a pay-as-you-go pricing model, where users are charged based on the amount of data processed. This allows for flexible usage and cost control. On the other hand, IBM DB2 typically follows a traditional licensing model, where users purchase licenses based on the number of users or the amount of data stored.

  2. Scalability: Google BigQuery is designed to handle massive datasets and provides highly scalable infrastructure. It can process petabytes of data and handle high query loads efficiently. In contrast, while IBM DB2 also offers scalability options, it may require additional configuration and management efforts to handle large volumes of data and queries.

  3. Ease of Use: Google BigQuery is known for its ease of use and simplicity. It offers a user-friendly web interface and supports SQL-based querying, making it accessible even for non-technical users. IBM DB2, on the other hand, may have a steeper learning curve and require more technical expertise to set up and manage.

  4. Integration with Other Services: Google BigQuery is tightly integrated with other Google Cloud services, such as Google Cloud Storage, Google Data Studio, and more. This seamless integration allows for easy data ingestion and visualization. IBM DB2, while it does offer integration options, may not have the same level of integration with other cloud services.

  5. Advanced Analytics and Machine Learning: Google BigQuery provides built-in capabilities for advanced analytics and machine learning. It supports running complex analytical queries, including window functions, machine learning models, and geospatial analysis. IBM DB2 also offers analytics capabilities, but it may require additional add-ons or configurations to achieve similar functionalities.

  6. Platform Support: Google BigQuery is a fully-managed cloud-based service offered by Google Cloud. It can be accessed and used from anywhere with an internet connection. IBM DB2, on the other hand, is available as an on-premises solution or a cloud-based offering. The choice of platform will depend on the specific requirements and preferences of the organization.

In summary, Google BigQuery and IBM DB2 have significant differences in terms of pricing model, scalability, ease of use, integration with other services, advanced analytics capabilities, and platform support. Organizations should evaluate these differences based on their specific needs and requirements before choosing a database management system.

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Detailed Comparison

Google BigQuery
Google BigQuery
IBM DB2
IBM DB2

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.

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

All behind the scenes- Your queries can execute asynchronously in the background, and can be polled for status.;Import data with ease- Bulk load your data using Google Cloud Storage or stream it in bursts of up to 1,000 rows per second.;Affordable big data- The first Terabyte of data processed each month is free.;The right interface- Separate interfaces for administration and developers will make sure that you have access to the tools you need.
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Statistics
Stacks
1.8K
Stacks
245
Followers
1.5K
Followers
254
Votes
152
Votes
19
Pros & Cons
Pros
  • 28
    High Performance
  • 25
    Easy to use
  • 22
    Fully managed service
  • 19
    Cheap Pricing
  • 16
    Process hundreds of GB in seconds
Cons
  • 1
    You can't unit test changes in BQ data
  • 0
    Sdas
Pros
  • 7
    Rock solid and very scalable
  • 5
    BLU Analytics is amazingly fast
  • 2
    Secure by default
  • 2
    Native XML support
  • 2
    Easy
Integrations
Xplenty
Xplenty
Fluentd
Fluentd
Looker
Looker
Chartio
Chartio
Treasure Data
Treasure Data
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 Google BigQuery, 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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