The value of data is highest at time of creation.
The following graph illustrates the importance of understanding the context surrounding the financial position of a customer, broker, competitor or even an entire market at any point in time — and why taking timely actions can be a value enabler. In other words, your teams need to be acting on the freshest data to impart the greatest value to your organization.
Based on Perishable Insights — Stop Wasting Money On Unactionable Analytics, Forrester Research, Inc.
That sounds good in theory, but the way most enterprises use data today makes it difficult for their teams to deliver value.
Companies suffer from database spaghetti: start with an open-source database, add another database for unstructured (JSON) data, then a cache, yet another database for search and complete the value antipattern by topping it all off with a data warehouse. The result: complexity, growing cost, latency and inaccuracy. Teams deliver unappetizing results because the intelligence they are acting on is based on cold data.
The first question you need to be asking is: How much potential revenue are you leaving on the table by basing your decisions on slow data? The next question is: When are you going to stop?
Read on. We have some ideas.
In search of business value: The need for speed
Putting the power of data to work delivering value across your organization calls for intelligent applications. Intelligent applications require unspooling your database spaghetti with an operational data platform that supports mission-critical applications with the freshest data.
At IBM TechXchange 2024, Kyle Basile, Director of Partner Sales at SingleStore, Mark Brooks, IBM Solutions Architect (StreamSets) and Anson Kokkat, IBM watsonx.data Principal Product Manager, discussed challenges organizations are facing, and a blueprint for transforming latency and complexity into substantial performance gains combined with lower costs.
In their session at TechXchange, “Making Real-Time Analytics and Decisioning a Reality with SingleStore, watsonx.data and IBM StreamSets,” the team shared the essential elements of a data platform to support intelligent apps:
- Immediate data availability. Ingests millions of records per second, makes data analytics-ready in milliseconds and scales out to meet any demand
- Fast analytics + hybrid search. Stores structured, semi-structured and unstructured data, run petabyte-scale analytical queries, perform efficient relational, keyword-based search, vector search and graph-based queries, and support multi-model data
- Enterprise data integration. Stream real-time events from stream processing frameworks like Kafka and Flink; leverage native integration with leading lakehouses like IBM’s watsonx.data and enterprise-level security/consistency including ACID compliance
Basile, Brooks and Kokkat then showed how combining IBM StreamSets, SingleStore and watsonx.data provides an intelligent data platform to drive real-time analytics and decisioning.
IBM StreamSets provides real-time streaming data
IBM StreamSets infuses intelligent apps with the power of streaming data — and data that is streamed intelligently.
StreamSets provides real-time data ingestion at scale so you can deploy reliable, smart streaming data pipelines across hybrid cloud environments at scale. StreamSets pipelines stream structured, semi-structured and unstructured data from any source. They also automatically detect and alert to changes in data structures and schemas, so you can seamlessly adapt to changing business requirements with zero downtime.
These intelligent data pipelines also adapt to unexpected data structural shifts with drag-and-drop, pre-built processors to automatically identify and adapt to data drift. The net effect is to substantially enhance your real-time decision-making, and reduce risks associated with data flow across your organization.
SingleStore provides millisecond insights on petabytes of data
SingleStore’s architecture and features serve as a great foundation for intelligent apps:
- SingleStore is a HTAP (hybrid transactional/analytical processing) database. Its Universal Storage combines both rowstore and columnstore within a single table to support mixed workloads efficiently. This lets you run transactional ( OLTP) and analytical ( OLAP) queries on the same dataset — without the need for separate databases or complex ETL processes. This reduces complexity and cost, and eliminates unnecessary data movement, unleashing unparalleled performance.
- SingleStore Pipelines. This feature offers fast ingest from multiple data sources including Kafka, Amazon S3 and HDFS. Combining this with the core SingleStore data engine enables single-digit millisecond response times on large datasets across hundreds of concurrent users running complex queries. Yep — the most challenging data needs for your most demanding customers aren’t quite so challenging anymore.
- SingleStore’s horizontal scalability. This enables scaleout architecture and the separation of storage and compute, giving you better price:performance. A related feature is SingleStore Workspaces, which, along with Pipelines, is among our most popular platform features. With Workspaces you can run multiple workloads on isolated compute deployments while providing ultra low-latency access to shared data. This ensures your apps are always operating on the freshest data. Workspaces can also adjust their own capacity thresholds up and down to meet changing workloads, keeping costs low.
- Flexible multi-model support is another advantage here. SingleStore supports relational, vector search, full-text search, time-series, geospatial — all in a single platform, as well as JSON and BSON documents. SingleStore provides fast K-NN and ANN vector search with IVF, HNSW and PQ algorithms, and full text-search for both fuzzy and exact text matching. Back to BSON and JSON, SingleStore actually offers the best of SQL + NoSQL, powering 100-1,500x faster JSON/BSON analytics for applications built on MongoDB®, and other document databases like AWS DocumentDB.
IBM watsonx.data simplifies and optimizes data for AI, enhancing price performance
IBM watsonx.data simplifies complex data landscapes, eliminates data silos and optimizes growing data workloads for price:performance while unifying, curating and preparing data efficiently for AI models and applications. IBM watsonx.data connects to storage and analytics environments, and accesses all data through a single point of entry with a shared metadata layer across clouds and on-premises environments.
SingleStore, IBM StreamSets + watsonx.data: The real-time, intelligent data platform is here
The wait is over. The real-time, intelligent data platform has arrived.
StreamSets streams data from anywhere including applications, databases, data warehouses and stream processing frameworks like Kafka and Flink. SingleStore's universal storage enables transactional,analytical and search queries at millisecond speeds. It natively integrates with watsonx.data and other data lakehouses, providing enterprise-level security and consistency capabilities including ACID compliance. AI is also front and center, as watsonx.data ensures data-readiness for AI applications and performs deep learning — while enhancing price:performance across environments.
“Applications built on this real-time intelligent data platform deliver unparalleled transactional and analytical performance, while cost-effectively scaling out to meet any demand,” said Nadeem Asghar, Senior Vice President and Chief Product Management & Strategy Officer, SingleStore. “This integration fulfills the vision of democratizing data by equipping all teams with the insights they need to deliver operational excellence and business value for their organizations.”
“This integration signals to the market that the time has come to transform businesses and entire industries with the power of data,” said Minaz Merali, Vice President, Product Management, Data Management, IBM. “This real-time intelligent data platform combines adaptive, intelligent streaming and real-time translytical performance with deep learning and AI to position companies for the future while winning in the marketplace right now.”
Use case: Helping one of the world’s largest companies respond to risk in milliseconds
After experiencing some serious challenges, one of the world’s largest enterprises determined the daily or hourly updates it has been receiving from its existing data infrastructure are not enough anymore. It needs to detect and respond to financial threats in milliseconds to react to risk events as they happen, make better decisions faster and stay within financial compliance thresholds (including regulatory ‘snapshots’) at all times. The solution is taking shape as follows:
- The team has implemented Apache Kafka as a streaming data source for all financial transactions
- Each transaction will be enriched by calling a risk-scoring service with a user-defined high-risk threshold
- IBM StreamSets will stream all high-risk transactions into SingleStore for real-time analytics, dashboarding and alerting
- All transactions will be written into IBM Cloud Object Store for deep analytics and AI using watsonx.data
Our teams are excited to see how this goes, and what kinds of technical and business results will accrue to this customer! I’ll be back soon with an update.
Having the best technology is table stakes, but as the preceding use case shows, customers using your technology to transform their businesses is where the rubber meets the road. So I invite you to explore "Fortune 25 Financial Services Giant gains vector-driven, real-time investment insights across petabytes of data with SingleStore"; "LiveRamp optimizes performance across Snowflake, Iceberg and all data in one platform"; and many other examples of real-time performance across massive data sets at Made on SingleStore.
