Many legacy database systems are not equipped for modern applications. Near ubiquitous connectivity drives high-velocity, high-volume data workloads – think smartphones, connected devices, sensors – and a unique set of data management requirements. As the number of connected applications grows, businesses turn to in-memory solutions built to ingest and serve data simultaneously.
Bonus Material: Free O’Reilly Ebook – learn how to build real-time data pipelines with modern database architectures
To support such workloads successfully, database systems must have the following characteristics:
Modern Database Characteristics
Ingest and Process Data in Real Time\Historically, the lag time between ingesting data and understanding that data has been hours to days. Now, companies require data access and exploration in real time to meet consumer expectations.
Subsecond Response Times\As organizations supply access to fresh data, demand for access rises from hundreds to thousands of analysts. Serving this workload requires memory-optimized systems that process transactions and analytics concurrently.
Anomaly Detection as Events Occur\Reaction time to an irregular event often correlates with a business’s financial health. The ability to detect an anomaly as it happens helps companies avoid massive losses and capitalize on opportunities.
Generate Reports Over Changing Datasets\Today, companies expect analytics to run on changing datasets, where results are accurate to the last transaction. This real-time query capability has become a base requirement for modern workloads.
Real-Time Use Cases
Today, companies are using in-memory solutions to meet these requirements. Here are a few examples:
Pinterest: Real-Time Analytics\Pinterest built a real-time data pipeline to ingest data into SingleStore using Spark Streaming. In this workflow, every Repin is filtered and enriched by adding geolocation and Repin category information. Enriched data is persisted to SingleStore and made available for query serving. This helps Pinterest build a better recommendation engine for showing Repins and enables their analysts to use familiar a SQL interface to explore real-time data and derive insights.
Novus: Portfolio Management\Novus supports more than 100 of the world’s top investment firms, helping users understand investment strengths and risks by providing a “moneyball-like” view of investment data. By taking advantage of a memory-optimized database system, Novus can deliver instant answers to hundreds of analysts querying their dataset.
Noah Zucker, Vice President, shares how Novus built a scalable portfolio investment platform in this video:
As more data comes online, organizations will rush to build systems that can rapidly ingest data while simultaneously making it accessible for analysis. To help you get there successfully, we teamed up with O’Reilly Media to publish an ebook on Building Real-Time Data Pipelines through In-Memory Architectures. Download it for free here: