Introduction
Over the last many years, analytical dashboards have proliferated enterprises from the boardroom to the manufacturing line. As businesses have become increasingly more reliant on analytics, end users have started to demand snappier performance from their dashboards. Gone are the days that visualizations are used simply for historical data. Data professionals are now using them for predictive and prescriptive analytics on petabytes of information, expecting the most real-time insights to tell their data stories.
Many organizations across consumer products, healthcare, fintech and other verticals have been met with the challenge of slow moving BI applications in recent years, due to the growth of data and many new types of users. Backend platforms are often retooled for each use case and still are unable to handle the growing concurrency demands of the business. This leads to applications that take too long to render and users that are faced with the dreaded “loading” pinwheel.
The Pain
The challenge of slow-running dashboards is not limited to legacy systems. Users experience slow dashboards when their applications are backed by legacy architectures as well as by more modern database systems. The pain points that enterprises face with legacy architectures start with the original intent of those systems: to support historical reporting and batch-oriented dashboards. Many databases from vendors like Oracle were built around a different era of business demands — when operational decisions were not made through data visualization. Over time, other (now legacy) systems like SQL Server emerged to help with transactional processing of data. This spread of new database systems purpose-built by workload introduced put even more stress on ETL technologies. The Hadoop era aimed to solve this, but ultimately consumers were left at the mercy of too many different systems for data to reside in and excessive data duplication across those silos. This made data storytelling very difficult.
The Challenge of Legacy Architectures
The struggle with legacy data systems has been even further exposed with the emergence of streaming data. Data is now being unlocked from chatty systems like e-commerce systems, financial trades, and oil and gas machinery. Real-time data pipelines from messaging infrastructures like Kafka, Kinesis, Pulsar and others have put an even greater burden on old, slow databases. Users expect the data from these applications to be readily available in real-time and operational dashboards, often combined with historical reference data. Instead, they end up stuck with dashboards struggling to load data for critical business decisioning.
Introducing Streaming Data
The challenges that legacy data platforms faced in the face of the growing base of analytics users has been met with the advent of modern data platforms. These platforms tout a new focus toward analytics, AI and machine learning. While modern data platforms still tend to specialize in OLTP or OLAP workloads, data platform vendors are adding even further delineation on the lines of data type — MongoDB specializes in document data, Redis for key-value data, etc. This approach works very well for application data that can often be unstructured and transactional. However, tying together multiple single-purpose databases for a unified visual experience remains extremely slow, and thus data stories end up very disjointed.
Cloud data warehouses have started to solve this problem, but scaling to satisfy an enterprise-scale analytics audience and handling operational data with platforms like Snowflake has proven to be extremely costly. This concern is primarily rooted in unpredictable compute costs as more and more users start accessing these dashboards. As more users come onto the platform, response times also become far less consistent. Ultimately, consumers end up losing the low latency response they were promised and organizations still end up spending more than expected.
Finally, organizations also try to solve their dashboard needs with a data federation approach. Federation vendors often tout the ability to have a single point of data access to all data sources. Unfortunately, this typically gets customers in more trouble due to 1) the high costs of procuring this technology and hosting it on very large servers, 2) a single point of failure, and 3) a newly introduced bottleneck which slows down dashboards even more.
Introducing Modern Data Platforms, and their challenges
The pains faced by dashboard users and the engineers behind them can very often be linked back to the monolithic legacy systems that they are architected upon, or often the overindulgence in new, single-purpose data platform technology. Accelerating those dashboards, and tuning your systems to be ready for fastboards requires a scalable, general purpose database built for fast data ingestion and limitless amounts of users. Singlestore Helios is built for fastboards.
Singlestore Helios
SingleStore’s database-as-a-service offering is called Singlestore Helios. Our platform is a distributed, highly-scalable, cloud-native SQL database built for fastboards. SingleStore is designed for highly performant ingest of any operational data, whether it is batch or streaming in nature. We make ingesting your data incredibly easy through SingleStore Pipelines, your way to get data from anywhere with five lines of SQL. Data can be brought from many different source systems like S3, Kafka, Azure Blob, etc. and streamed in parallel to make your data instantaneously queryable upon ingest.
SingleStore offers a singular table type for all of your data storytelling needs. SingleStore’s architecture allows you to support large-scale Online Transaction Processing (OLTP) and Hybrid Transactional and Analytical Processing (HTAP) at a lower total cost of ownership. It is a continuing evolution of the columnstore, supporting transactional workloads that would have traditionally used the rowstore. With the ability to seek millions of rows at a time, scan billions, and compress your data 10X — SingleStore is the ultimate solution for fastboards. Most importantly, Singlestore Helios is a converged data platform that can store data of any type, JSON, time-series, key-value, etc — all with SQL.
Here at SingleStore, we believe many modern data platforms can coexist. Many of our customers leverage in-memory, NoSQL technologies for their mobile applications and relational, cloud EDWs for long-term storage of their data. However, when it comes to accelerating the most critical business insights driven by analytics, AI and machine learning, they turn to SingleStore.
Our Singlestore Helios can help you extend your legacy platforms like Oracle and Teradata with fast ingest and querying, complement Snowflake and BigQuery without unpredictable costs, and onboard any new type of data without the constraints of NoSQL databases.
Singlestore Helios: The Data Platform for Fastboards
What kinds of dashboards?
As discussed, SingleStore is a fantastic choice for your most important visualization and analytics needs. The following section goes a bit deeper into exactly what types of dashboards SingleStore is powering today, and some of the important concepts.
SingleStore is the core data platform for many analytical applications:
- Real-Time Analytics
- Operational BI
- Historical Reporting
- ML-Driven Analytics
- Ad-Hoc Data Discovery, and many more
Real-time dashboards are used to make critical business decisions in the moment. They often require sub-minute or sub-second latency, and are highly relevant in preventative maintenance, inventory and IoT applications. These types of workloads benefit greatly from SingleStore’s streaming ingestion, built-in predictive capabilities, and scalable analytics. SingleStore’s ability to quickly ingest high volumes of data, rapidly run predictive models for scoring, and store for fast retrieval makes it best in class for real-time dashboards. Medaxion leverages SingleStore to achieve hospital event to insight within 30 seconds.
Historical Reporting dashboards encompass both the most recent data as well as long-term insights. They are often found supporting financial reporting and historical sales analytics use cases. SingleStore offers a number of different features that accelerate historical dashboards — drop-in SQL compatibility, scalable analytics functions, and built-in machine learning to name a few. SingleStore makes it extremely simple for dashboard developers to deliver high-quality, fast historical analytics for end consumers. Kellogg uses the SingleStore platform to accelerate the speed of their Tableau dashboards by 80X.
Over time, we have seen machine learning and AI emerge to the forefront of analytics ecosystems and thus, visualizing ML through visualization has become a common way to share the performance of models. These dashboards are meant to provide a beautiful visual interface for otherwise complex data science workflows. They are often used to help executives and business people align with an organization’s predictive capabilities. At SingleStore, we see many of our customers leveraging our ML-enabled pipelines to score and visualize data in real-time. Users also leverage built-in predictive functions and our SingleStore capabilities to perform training and testing at large scale.
Tools
Having discussed many different variations of fastboard use cases, it is also important to address the vast landscape of tools and technologies that enable these. Furthermore, there is no feature more important for a database than seamless, performant connectivity to every dashboarding tool. SingleStore is wire protocol compatible with MySQL making it instantly accessible from any BI tool such as Tableau, PowerBI, Looker or IBM Cognos Analytics, and with widely-available bindings to popular programming languages such as R and Python. We also have native connectors for many dashboarding tools, purpose-built for accelerating the speed of your dashboards. Many of our customers have also found success custom building their dashboards using frameworks such as React, in architectures where SingleStore acts as a performant API-backend.
Summary
Here at SingleStore, we understand that fastboards may come in all shapes and sizes, and that each use case is unique. As discussed above, our ability to approach more fastboard use cases than any other database is rooted in our native SQL compatibility, SingleStore engine, streaming ingest and vast predictive capabilities.
Singlestore Helios can empower modern data platforms with fastboards in order to achieve faster, more informed decisions, and improved customer experiences. We invite you to explore how some of our customers are powering their fastboards to tell rich data stories here.
