Databases—the systems that organize and store data—form the foundation of every analytics strategy. Getting the structure and foundational architecture of your database systems right can make the difference between a well-supported structure that provides good value and one that collapses under its own weight.
Databases typically come into play in the second stage of the data pipeline: data processing (the “prepare and store” stage). Analytics applications and platforms use the information contained in databases to help organizations understand the past and predict the future.
From banks that analyze financial transactions to detect fraud to smart agriculture companies that use videos to reduce pesticide use, organizations need databases that are optimized to perform for the tasks at hand. For organizations selecting database software and systems, it’s critical to choose technology that works effectively for the problem being solved.
Operating your database smoothly depends not only on software, but on hardware. Having the right infrastructure in place—including different types of compute (CPUs, FPGAs, and accelerators), storage, memory, networking, software libraries, and Java optimizations—can drive improved database performance and easier database management.
DBMS software makes it possible to store and retrieve information in a database. DBMS software includes not only a user interface to allow interaction with the database, but optimizations that prioritize workloads and help make access faster.
Popular DBMS software includes Oracle, SAP HANA, Microsoft* SQL Server, Splunk, and Apache Cassandra. Every DBMS uses specific types of data structures—like trees, arrays, stacks, and graphs—to organize and more effectively manage data.
Types of Databases
Enterprise analytics works to extract value from many types of data from many sources. Optimizing an analytics strategy requires starting at the database level and choosing a DBMS that will work effectively for your specific business needs. Significant trade-offs exist between consistency, availability, and partition tolerance, and no database technology can deliver on all three. This concept, known as the CAP theorem, means that it is necessary for organizations to choose which database strengths are most important for their particular business needs.
Databases may be hosted on-premises or in the cloud. Cloud databases are known for scalability, but some businesses prefer to keep data on-premises in order to have more control over security, especially in regulated industries.
Your programming language defines data structures and is critical to manipulating and analyzing data. Different database products and types use programming languages optimized for specific data types, functions, and use cases. Many large companies will need several types of databases to organize and employ their data effectively.
Relational databases, based on standardized data tables that express relationships between data, commonly use structured query language (SQL). Relational databases are highly effective for managing structured data with consistent rules and relationships—like financial transactions or inventory tracking. Relational database software includes Oracle, Microsoft* SQL Server, IBM DB2, and Azure SQL.
OLTP (Online Transactional Processing) is a category of data processing that is focused on transaction-oriented tasks. OLTP typically involves inserting, updating, and/or deleting small amounts of data in a database.
One of the most common applications for databases is transaction processing. OLTP is a mode of accessing databases that is geared specifically for transaction processing with many simultaneous users. OLTP is a common way to use Oracle, IBM, and Microsoft databases.
To review a large amount of historical information for analytics purposes, businesses may use online analytical processing (OLAP). OLAP queries typically use a multidimensional data model, though some also use relational data models. Data warehouses are a specialized type of OLAP designed specifically for analytics.
In an object-oriented database, information is represented as objects and classes of objects. A hybrid form of object-oriented and relational databases is called an object-relational database.
Sometimes called NoSQL databases, non-relational databases break free from the table structure. Typically using metadata for organization, these databases are effective for managing non-structured data and complex data types like images and video. MongoDB and Apache Cassandra are examples of popular non-relational database software.
- Key-Value Database
Sometimes called a key-value store, this is the simplest form of a NoSQL database. Redis and Oracle NoSQL Database are both key-value databases, which use a hash table to store and retrieve data using a unique identifying “key".
- Wide-Column Stores
In wide-column stores, data is stored in columns of related information. Cassandra is the most common of these databases, which offer scalability and fast queries for large data sets.
- Document Databases
Sometimes called document stores, these store data as complex records called “documents,” which include metadata or information about the data itself. Documents can include any type of data, including images and video.
- Graph Databases
Another type of NoSQL database, graph databases are based on graph structures to define relationships and store data. Graph databases are designed to allow fast queries and high-volume data processing for highly connected information. SAP HANA and OrientDB both use graph database models.
Innovations from Intel, from processors to libraries and Java optimizations, drive improved database performance and easier database management for organizations worldwide.
Intel® Technologies for Database Management
Optimizing databases that use massively scaled data sets requires hardware that can effectively support database and analytics workloads.
Compute and memory must work together in a highly performant way, with processing instructions that keep queries and data streams moving quickly. Data storage and access depend on tiering that automatically prioritizes time-sensitive and critical workloads.
Intel drives innovation at the silicon level, incorporating instructions like AVX-512 and TMUL to accelerate data processing.
In addition to supporting databases with hardware designed with performance in mind, Intel works to enhance the development of open source software development. An entire team at Intel is devoted to Java optimization, with the goal of accelerating development across the open source and database application developer community.
Getting Databases Right for Optimized Performance
An effective analytics strategy depends on having the right database technologies working with the right types of data. As your analytics strategy matures to use more types of information across more applications, it is likely that your organization will use many types of databases and multiple database vendors.
With our wide range of hardware products and features designed with databases in mind, as well as software libraries, tools, and optimizations, Intel is committed to optimizing database management. From silicon to software development, Intel works to support the biggest database technology names today and foster innovation for tomorrow.