As technology continues to transform modern workflows, data analytics have become an irreplaceable source of actionable business intelligence for a variety of industries. In the past, financial analysts and market researchers were the primary beneficiaries of real-time reporting and analysis tools, but recent advancements in machine-generated data sources have increased the demand for comprehensive software solutions across the board.
A recent article from CIO pointed out that expectations for database systems are significantly expanding to include non-traditional storage and analysis models that are capable of processing large volumes of data without sacrificing performance. This shift in expectations has prioritized features that are often difficult to integrate with relational database technologies and has generated a lot of interest in alternative use cases and workloads.
Companies are now aggregating more data than ever before, yet growing concerns over storage requirements and query performance have led some enterprises to question whether their databases are adequately optimized. This has created greater market awareness about the importance of instant and customized access to business data, especially for industries that want to leverage ad hoc reporting and analysis features as part of their core operational strategies, such as telecommunication, digital marketing and mobile gaming. As a result, enterprises are moving away from conventional OLTP relational databases in search of more convenient and efficient software tools — that’s where columnar databases come in, demonstrating why Infobright DB remains such a powerful solution for market leaders in these spaces.
While relational (row-oriented) databases are quite effective for transactional applications, they have a markedly difficult time running analytics against large data volumes. When a user runs a query, a conventional database will retrieve the entire row, rather than the specific information that is needed. Queries become increasingly inefficient as the volume of data grows, which can significantly impact performance and create unnecessary hold up.
The issue is that row-oriented databases store all values of a data record as one entity, forcing analysis applications to read each record in its entirety, even if a user is trying to access a single attribute. Additionally, relational databases require a high degree of effort to maintain, often involving the creation of indexes or partitioning data.
In contrast, columnar (column-oriented) databases retrieve, analyze and return only the attributes a user is looking for.. This approach can significantly reduce I/O and lower response times, even when a user creates a complex ad hoc query of large data records. Most columnar databases are designed for high-performance analytics, yet they can lose some performance advantages when dealing with massive data stores.
To retrieve the specific values a user needs, column-oriented databases must read and discard a number of data records that do meet the query parameters. This can be somewhat mitigated through row-style tuning schemes and data partitioning, yet these correction methods are often ineffective for near real-time environments that rely on high volumes of machine-generated data. The truth is, not all columnar databases are created equal, which explains how Infobright DB has achieved such a high degree of market relevance.
Columnar databases have generated a lot of interest with mainstream audiences over the past few years, yet the technology has been commercially available as far back as 2004. In 2017, Gartner released a survey showing that production deployments of columnar technologies had increased for four consecutive years and showed no signs of slowing down. The surge of interest has pushed many IT companies to create new products aimed at meeting the current market demand, but few solutions have had time to mature alongside the needs of enterprise users.
Infobright DB merges the high-performance capabilities of columnar databases with a unique Knowledge Grid architecture that has been optimized for BI analytics. Not only does this enable maximum performance when querying large volumes of machine-generated data, but it also removes the need for database tuning and administration tools by placing a knowledge grid directly on top of the core storage system. Through the integration of semantic intelligence with advanced compression technology, Infobright DB is able to supercharge queries while reducing a company’s hardware footprint and minimizing the need for excessive storage and server capacity. Some of the technology’s core benefits include:
Enhanced query performance: Storing highly compressed data in columns can reduce load times without sacrificing accuracy.
Rapid installation: Users can fully install Infobright DB in minutes without the need for configuration and index creation.
Near real-time data access: New records are immediately available for complex ad hoc queries with no impact on load speeds.
Lower server, storage and administration costs: IT administrators save time on indexing, data partitioning, data duplication and database tuning.
Infobright DB is particularly well-suited to environments where queries frequently change or expand across large volumes of complex machine-generated data. The Knowledge Grid architecture allows Infobright to understand the data it organizes and what region of the hard drive it is stored within, leading to quicker query responses for a wide range of enterprise applications, such as:
As companies seek out new ways to bolster their decision-making capabilities and improve their organizational agility, the demand for high-performance analytic databases will continue to drive innovation within the tech sector. If you’re interested in leveraging cutting-edge applications as part of your BI strategy, Infobright DB can provide the solutions you need to remain competitive in today’s data-driven marketplace.
Find out more about the Ignite Infobright DB platform and how we can store and analyze large volumes of big data, and perform interactive and complex queries for better and faster business decisions.