February 2019 Summaries

Eric Hanson, Principal Product Manager at SingleStore, discussed scalable SQL and how it can solve legacy database limitations for time series data. He highlighted SingleStore's capabilities in supporting high ingest rates, low-latency queries, and concurrency needs. With its built-in support for schema and ANSI SQL, SingleStore is a strong candidate for time series workloads. The database has features such as fast query processing through vectorization and compilation, excellent data compression, and powerful SQL Window function extensions that are ideal for time series data. Additionally, SingleStore provides full SQL capability, transactional consistency, backup and restore, cluster management, rowstore indexes, columnstore concurrency, high availability, and the ability to handle general-purpose applications like data warehouses and operational data stores. The database is highly scalable with a scale-out architecture, making it suitable for large workloads and large data volumes.

The growth of time series data is driven by advancements in sensor technology, online and wireless connections, better databases, responsive websites, and analytics tools. Time series data has various applications across industries such as device monitoring, energy systems, manufacturing, computer operations, financial pricing and trading, and marketing automation. It enables real-time response to signals, alerting, and monitoring. The life cycle of time series data involves generation, pipeline processing, transformation, and analysis. A suitable database should support transactions, scalability, operational capabilities, and analytics support. SingleStore is highlighted as a suitable option for time series databases due to its ability to handle fast data ingestion, support for SQL, and scalability. Fanatics, a user of SingleStore, showcases the application of time series data in their business, using it to predict jersey sales and gear up production accordingly. The webinar also covers comparisons with other databases like Redis and Snowflake.

SingleStore is a fast, scalable SQL database that can be used alongside Looker to create a fast, scalable analytics solution. Both SingleStore and Looker are flexible and powerful tools that work well together, with SingleStore's ANSI SQL support and Looker's ability to connect to any SQL data source making them a strong combination for analytics needs. When paired together, they deliver consistent and concrete results, such as creating real-time dashboards, speeding up analytics performance, and providing near-real-time or real-time analytics. Fanatics, an online retail company, uses SingleStore to create a fast and reliable data architecture for all their analytics needs. Looker can also be used alongside existing BI and analytics tools, fostering a true data culture at organizations. By using SingleStore and Looker together, users can achieve near-real-time or real-time analytics, access their data more easily, and speed up performance. The combination of SingleStore's rowstore and columnstore functionality with Looker's optimized database interface makes them well-suited for large-scale analytics use cases. With the ability to work seamlessly together, SingleStore and Looker support a broader range of data ingest, transaction processing, and analytics needs, making them a powerful solution for organizations looking to improve their analytics performance.

The `diwo` platform, an AI-powered tool, uses SingleStore to power its conversational mode and provide real-life business challenges with cognitive decision-making capabilities. The software aims to bridge the gap between data science research and practical business applications, empowering users to make timely decisions. With a distributed microservices architecture, diwo's ASK persona utilizes SingleStore for transactions and queries, leveraging the database's speed, scalability, and flexibility to support its high-performance requirements. The platform's development team was initially attracted by Redis and Cassandra but found them inadequate due to performance and query composition issues, leading them to adopt SingleStore as a natural fit for their needs. With SingleStore, diwo can handle large datasets, offer interactive querying capabilities, and provide industry-agnostic cognitive decision-making architecture. As the platform continues to grow, it is likely to further utilize SingleStore's features to support its mission of making business decisions more accessible and efficient.

GoGuardian is an Education Technology company that uses machine learning to moderate student web activities and improve the learning environment. The company faces significant challenges in handling the vast amount of data generated by its users, including 5 million students per day. These challenges include data duplication, high throughput, and aggregate queries that are not suitable for traditional relational databases. To address these issues, GoGuardian experimented with various solutions, including sharded SQL databases, Druid, Phoenix, BigQuery, Presto, Athena, Spanner, and finally SingleStore. After testing and evaluating the performance of each solution, GoGuardian found that SingleStore was the most suitable option for its use case due to its ability to perform joins and unions across row and columnar tables, high availability, speed, and friendly support from the vendor's engineering team. By adopting SingleStore, GoGuardian was able to improve its infrastructure's performance, reduce costs, and enhance its customers' experience.