Optimizing Data Locality for Efficient Data Management
Written by Lynn Orlando
Published on March 31, 2020
What is a Data Locality Optimizing Algorithm?
Data locality is a basic computing principle that assumes access to certain data is required to complete certain tasks. The idea is to move the computation closer to where the data lives rather than moving massive amounts of data to the computation. A data locality optimizing algorithm helps ensure that the computation is as close to the data as it can be. There are several ways to implement a data locality optimizing algorithm, which we discussed in an earlier blog post.
Data locality is important because it drives efficiencies and impacts performance significantly. These efficiencies are especially important when dealing with large amounts of unstructured data. The use of optimizing algorithms increases the throughput of your system, decreases congestion across your network, and allows users to access useful information faster.
Industries that Rely on Data Locality Optimization
Data locality optimization algorithms are used across a variety of industries. They are especially useful in industries where modern organizations must store and access a massive variety of less structured data (e.g., mobile data, click-streams, images, videos, server logs, sensor data, location coordinates, and more) that has to be captured, synthesized and analyzed in real-time. Some of those areas include:
- Genomic Data Analysis (Read Solution Brief)
- Cryo-EM Analysis (Read Solution Brief)
- Media & Entertainment (Read Solution Brief)
The most significant data management challenge for most of these companies is speed. The data they are dealing with must be stored, organized, and accessed with speed and efficiency. For Media & Entertainment companies, effective data locality and data management can significantly impact the time it takes to get from filming to the big screen. We wrote about the need for a new file system for M&E earlier on the blog here.
For healthcare, making sure doctors, researchers, and staff can get the information they need when they need it could mean saving a life. Data locality optimization algorithms allow users to access patient records, research data, analytics, and more with ease. Networks in these companies must have the ability to handle massive throughput without clogging up the network. Optimization algorithms increase throughput and prevent “traffic jams” by moving data where it needs to go more quickly.
Enterprise Storage & Data Management
One hurdle with enterprise storage is that large volumes of data are stored with many users trying to access it at the same time. At the same time, unstructured data is generated at astounding rates. Because of the sheer volume data, company networks often experience latency in being able to access the information. The network becomes congested with so much information and requests for data that it can be difficult for users to access what they need when they need it. Implementing a data locality optimization algorithm reduces the amount of work required to transmit data from one node to another.
The use of data locality optimization algorithms is beneficial for improving workflow processes and increasing overall production and efficiency. Waiting for data can consume valuable time that hinders forward progress and wastes resources, especially if your staff must spend large amounts of time, multiple times a day waiting to retrieve needed information. (See how one UCLA researcher increased his team’s time to productivity by using the Stellus Data Platform with its data locality optimizing algorithm.)
The Stellus Data Platform is designed with speed in mind. Based on our innovative Key-Value over Fabrics architecture with data locality optimization algorithms built into the software, the platform delivers unmatched throughput and performance. If your organization is looking for ways to more efficiently manage your data throughput, storage, and retrieval accuracy, let’s connect.