SME Solutions Group helps institutions manage risks and improve operations, and they’ve chosen SingleStore as a database partner. Their premier services include data analytics and business intelligence (BI) tools integration, areas where SingleStore adds tremendous value. In this webinar, SME Solutions Group describes how they use SingleStore to solve a wide range of problems, including big data and fast data needs for utilities.

How SingleStore Fits SME

Mike Czabator, a systems engineer with SingleStore, describes SingleStore as a fully relational, fully distributed, memory-first database. It loads data live; scales out to support arbitrarily large numbers of ad hoc SQL queries, BI tools users, application users, machine learning (ML) models and AI algorithms. There is no delay between data ingest and data availability for queries, which can span historical and current data at terabyte and petabyte scale.
Cloud, AI/ML, big data and more
SingleStore supports streaming ingest, rowstore tables that run in memory – traditionally used for transactional (OLTP) applications – and disk-based columnstore tables, widely used in analytical (OLAP) applications. With SingleStore, queries can scan rowstore and columnstore tables, and there is no time-consuming, fraught extract, transform, and load (ETL) process; instead, SingleStore Pipelines move data around.
SingleStore is MySQL wire protocol-compatible and connects to Kafka, Spark, and many other data sources and destinations. SingleStore runs on-premises, in the cloud, in containers, with SingleStore’s Kubernetes Operator, and as a managed service.
Converged transactions and analytics
One of the critical applications for SingleStore is speeding up dashboards created by BI tools, or custom-built; analytics-driven applications, such as Uber ride hailing; and ML and AI applications, such as Epigen, which works with government and business and government clients. This is a perfect fit for the analytics and BI tools integration work done by SME Solutions Group.

Building the Ideal Data Ecosystem

Ron Katzman is Director of Strategy & Operations at SME Solutions Group. He describes current industry trends that the company helps customers align themselves with: data-driven decision making; the need for predictive analytics rather than reactive reports; and the need for speed, agility, and flexibility.
IoT is crucial for utilities; so is AI
In the energy industry, key needs include energy storage, cybersecurity, outage management, and distributed, rather than centralized, energy resources. These areas are ripe for IoT, machine learning and AI, and other emerging technologies. But utility companies still need help with strategy and implementation.
Hadoop has limited security
Traditional data warehouses are simple but slow; the “event to insight cycle time” is long. Today, architectures are complex. The use of NoSQL solutions, as described in SingleStore’s Hadoop/HDFS case study, adds some capability, but also adds both complexity and cost.
SingleStore's convergence offers 10x the performance at one-third the cost
SingleStore offers a converged solution. It has nearly unlimited capability, fast ingest, and fast queries. Unlike Hadoop/HDFS and the whole NoSQL world, it has native support for ANSI SQL, leveraging existing developer skills. SME describes SingleStore as the heart of a modern digital transformation ecosystem, offering 10 times the performance of competing solutions at one-third the cost.

Utility Case Studies

There are several existing case studies for SingleStore client solutions in the energy industry. A Top 10 US utility streamed data through SingleStore for real-time analytics. They started identifying theft within 10 days of going live with SingleStore. Processing time for one legacy job dropped from 22 hours to less than 20 seconds. Ironically, the use of SingleStore increased the lifespan of existing platforms, since emerging, complex tasks could be offloaded to SingleStore.
Scalable analytics platform based on SingleStore
Another top energy company used SingleStore for data integration across billions of data points, with fast, efficient analytics running on top. Quality of service has improved, leading to happier customers. And a leading US energy company mitigates more than $1M a day in drilling costs using machine learning, taking out SAP HANA for a much faster, lower-cost solution based on SingleStore.
SME Group summarizes SingleStore as fast, scalable SQL
SME describes SingleStore as “the fastest thing on the planet”; simple, high-performance, low-cost, and very flexible.

Q&A, and More

Two questions were answered at the end of the webinar. SME has seen time to value as short as two weeks with SingleStore. And, a user asked about the comparison of SingleStore to Snowflake. SME describes Snowflake as a fine database, but limited to the cloud, which is not always the favorite for utilities. SingleStore is unmatched on ingest capability, among other attributes, and gets the job done at a lower price point than other solutions.
The webinar also describes a detailed case study of a SingleStore implementation that supports a new meter deployment with much greater data ingest and processing requirements. We’ll share a deep dive into this case study in a future blog post.
In the meantime, you can schedule a demo with the SME Solutions Group; download and run SingleStore for free; or contact SingleStore.