How do you recognize poor data experiences?
You may recognize them when your application or dashboard  doesn’t refresh properly. Or your supply chain application reflects data from a few days, instead of a minute ago. Or your ride-sharing app doesn’t reflect accurate pricing. Or you’ve missed identifying a fraudulent payment.  The truth is, poor data experiences don’t just impact you — they impact your customers, the end users you’re working diligently to serve.
Today’s users don’t just demand data, they demand real-time visibility and insights. They demand the immediacy that empowers them to make fast, informed decisions that keep their worlds moving.
‘Real time’ doesn’t happen through sheer will. It’s the outcome of a foundation — a data architecture designed to drive instant analytics. Yet, legacy platforms can’t handle fast-moving and fast-changing data streams. They simply weren’t designed to keep up. And, too many databases cause a fragmented, overly complex look at analytics.
To truly deliver on the promise of  real time, these workloads demand a key set of requirements from the underlying data platform:
  1. Streaming Data Ingestion. Data needs to be continuously ingested from diverse sources as it's generated, and be immediately available for indexing and querying. Batch data loading or ETL  isn't good enough.
  2. Low Latency Analytical Queries. The data platform needs to be  able to deliver on stringent SLAs for latency (100s of ms or less) on complex analytical queries serving interactive applications, dashboards and APIs
  3. Flexible Indexing. The platform should offer  the ability to enable low-latency data access in a variety of scenarios including selective queries, full-text search queries, geospatial queries and more.
  4. High Concurrency. Apps are often customer facing and experience spikes in usage — and your data architecture should support millions of real-time queries across tens of thousands of users. 

The Challenge: Building Real-Time Applications & Analytics

Challenge 1: Single-node databases
Businesses at the start of their real-time application journey often start with single-node, open-source databases like MySQL, PostgreSQL, MongoDB, MariaDB and SQL Server. While these databases initially provide a good foundation, users quickly hit a single-node stall . That is, these legacy databases aren’t optimized for analytics — much less real-time, interactive analytics required to power real-time applications.
Challenge 2: Data Warehouses
Today’s data warehouses power business intelligence (BI) and reporting workloads that enable organizations to quickly aggregate and analyze large amounts of data from multiple sources to drive insights.  But data warehouses aren’t optimized for low-latency analytics or large numbers of concurrent users — especially when dealing with fast-moving, streaming data from diverse sources that power modern applications and drive insights in real time.
A new approach to powering real-time analytical applications requires reducing time-to-insights for fast analytics on dynamic data for complex queries, all within sub-seconds.
The Solution: SingleStoreDB, The Database for Real-Time Applications & Analytics 
The formula for a real-time system is simple: It needs to be  fast, unified, scalable and resilient. 
Built with a unique three-tiered storage architecture and designed for millisecond response times, SingleStoreDB is the world’s  fastest distributed SQL database for real-time analytical applications. By combining transactional and analytical workloads within a multi-model structure in a single engine, SingleStoreDB eliminates performance bottlenecks and unnecessary data movement. We’re the crossroads where highly performant meets highly powerful.
Even more, SingleStoreDB meets — and exceeds — the key features and requirements necessary to truly be a real-time analytics database:
  • Streaming Data Ingestion: Using SingleStore Pipelines, organizations can drive high-throughput, parallel, exactly-once streaming ingest of millions of events per second from diverse data sources including Apache Kafka, blob stores (S3, Azure blob storage, GCS) or HDFS
  • Three-Tiered Storage: Our Universal Storage format brings together the fast table scan performance of a columnstore and seek performance of rowstore, together with cloud object storage.
  • Unified Architecture: SingleStoreDB unifies transactions and analytics in the same engine to deliver high-performance analytics on operational data in real time — without the need for data movement
  • Reliable Support for Complex Queries: SingleStoreDB's SQL query processor matches the performance of top data warehouses on popular analytical benchmarks, including TPC-H and TPC-DS. SingleStoreDB can also match the performance of pure-play operational databases for TPC-C.
  • Strong High Availability: Your applications should stay online and be highly available — even when facing hardware failures or day-to-day management operations like database upgrades and schema changes.
  • Separation of Storage & Compute: Applications don't need to give up elasticity or unlimited storage to get low latency ingest and query capabilities.

Real-Time Analytics & Applications Use Cases 

The need for real-time analytics and applications is prevalent across a wide variety of industries and use cases, including:
Adtech: Marketing segmentation and ad targeting based on data from various sources
Cybersecurity: Security threat detection and analysis over device telemetry data
Fintech: Low-latency stock portfolio analysis based on fresh market data for high net-worth customers
FinServ: Credit card fraud detection over a stream of purchase data and other telemetry
Energy & Utilities: Analysis of sensor data from oil wells to detect maintenance issues early, guide the drilling process and do profitability analysis
Gaming & Media: Behavioral analysis on the click traffic from web games or streaming video services to optimize end-user experience (like providing more personalized recommendations)
IoT & Telematics: Ingesting and analyzing IoT event streams or video streams for anomaly detection 
Martech: Faster time-to-insights for publishers and advertisers to manage their performance marketing channels for revenue generation
Retail & eCommerce: Reduce latency from 24 hours to minutes, while making data available from external sources
Supply Chain Analytics: Using real-time sensor data with predictive analytics to power the future of connected supply chains

Unleash the Power of Your Data With SingleStoreDB

SingleStoreDB is the world’s fastest distributed SQL database for real-time analytics and applications. Ready to see for yourself? Try SingleStoreDB free today.