Streaming analytics platforms can ingest, analyze, and act on real-time streaming data coming from various sources, so you can take immediate action while the events are still happening. They can gather and analyze large volumes of data arriving in "streams" from always-on sources such as sensor data, telematics data, machine logs, social media feeds, change data capture data from traditional and relationship databases, location data, etc.
The difference between streaming analytics and traditional analytics lies in when data gets analyzed. Traditional analytics follows a store-first-then-analyze-paradigm, where we first store the data and then analyze it for deriving insights. Traditional analytics is also mainly applied to data at rest. In streaming analytics, we analyze the data first while the events are still happening and then store the relevant data for batch analysis. This allows streaming analytics platforms to handle the scale and constant flow of information and deliver continuous insights to users across the organization.
What are the Benefits of Streaming Analytics?
The top benefits of streaming analytics are:
Improve operational efficiencies: Enables data and analytics team to understand ongoing events faster and act on them immediately. In addition, the operations team can get access to continuous data to track and work on the KPIs to keep the operations running.
Reduce infrastructure cost: Streaming analytics and ingestion cloud services can help your organization manage workloads efficiently by auto-scaling the clusters to optimize costs.
Provide faster insights and actions: An end-to-end, AI-powered streaming analytics solution can ingest any data from any source at any latency to process and operationalize it for faster and continuous insights to all users.
Essentially, streaming analytics is all about extracting business value from data in motion in the same way traditional analytics tools use data at rest. Organizations in every industry have data streaming available from applications, social media, sensors, devices, websites, and more sources. You need flexible, scalable tools and processes to make the data accessible and usable at the moment it's needed.
For example, real-time streaming analytics can instantly help a company issue alerts when a customer's experience is degraded or when fraud is detected. As a result, the customer gets a better response and service experience. In addition, the company can act before a minor outage or incident grows into a broader, more serious situation that may cost more time and money to resolve and potentially harm their reputation.
Additionally, businesses can use information derived from real-time analytics to identify anomalies and business changes (e.g., a sudden spike in demand for a product or service, a defect in manufacturing) as they occur. Such information allows companies to take instant action and seize an opportunity that they otherwise might miss.
The streaming analytics platform vendors typically consist of open-source Apache projects like Spark, Kafka, Storm, and Flink. It also includes proprietary vendors like IBM, TIBCO, SAS, Software AG, Amazon Kinesis, Azure Event Hub, and Google Pub/Sub. Informatica Streaming and Ingestion solution is a code-free or low-code stream processing solution that helps organizations connect to all the open-source and commercial streaming vendor's solutions to ingest, process, and operationalize the streaming data for advanced analytics and AI consumptions.
Informatica Stream Processing
Informatica offers stream processing as part of the Intelligent Data Management Cloud , a cloud-native data management platform. The Intelligent Data Management Cloud also delivers several other services, unified metadata and AI layer (CLAIRE), and over 10,000 metadata-aware connectors that cover all three major public clouds (among other things).
In addition, Informatica has partnered with Datumize to maximize its compatibility with IoT. The platform also offers comprehensive cloud capabilities, and is available on a consumption-based pricing model.
Figure 1 - Reference architecture for the Informatica Intelligent Data Management Cloud
Moreover, the Intelligent Data Management Cloud provides a single architecture (and user experience) for data ingestion – whether that data is being ingested via streaming, batch, or whatever else – leading to stream processing and data integration solutions. This architecture is shown in Figure 1. Features relevant to streaming include: real-time ingestion, mass ingestion, automated handling of schema and data drift, and a Kappa messaging architecture.
Informatica's solution for stream processing consists of several different products and services that combine within the singular platform of the Intelligent Data Management Cloud. The backbone of this solution comes in the form of three services: Cloud Mass Ingestion, High-Performance Messaging for Distributed Systems, and Data Engineering Streaming. Between them, these provide – respectively – streaming ingestion, high-speed messaging, and stream processing. Other Informatica offerings, such as Cloud Data Integration and Enterprise Data Catalog, can add to this core. Taken as a whole, the Intelligent Data Management Cloud lets you ingest streaming data and move it to wherever it needs to be while processing, transforming, and governing it as necessary.
Looking at the first of these services in more detail, Cloud Mass Ingestion provides format-agnostic data movement and mass data ingestion, including file transfer, CDC (Change Data Capture), and exactly-once database replication. It also offers mass streaming ingestion from various sources, complete with real-time monitoring, alerting, and lifecycle management.
Moving on, High-Performance Messaging for Distributed Systems is what it says on the tin: a performant messaging system boasting "ultra-low latency", targeted at distributed systems. In addition, it provides high resiliency and guaranteed message delivery. Alternatively, you could leverage Kafka (or another messaging service, but Kafka is the most well-known and well-supported) using Informatica's connectivity options. For instance, Cloud Data Integration can gather and load batch data from Kafka directly, with an "at least once" delivery guarantee. Enterprise Data Catalog can also scan Kafka deployments to extract relevant metadata (message structure, for instance).
Finally, Data Engineering Streaming is a continuous event processing engine built on Spark Streaming that is designed to handle big data. It supports batch and streaming data and features out-of-the-box connectivity to various messaging sources and no-code visual development (see Figure 2). As part of the latter, it provides hundreds of prebuilt transformation functions, connectors, and parsers. You can also pipe in your code or build your functions and whatnot using Informatica's business rules builder. Essentially, it allows you to enrich your streaming data in real time. This could mean improving data quality, masking sensitive data, aggregation, or what have you.
Figure 2 – Informatica Data Engineering Streaming pipeline
IDMC also supports Spark Structured Streaming, which can be important if you want to aggregate streaming data based on event time (as opposed to processing time) and hence reorder data that has arrived out of order before delivering it to your data target. It also supports Confluent Schema Registry, which can parse Kafka messages, retrieve message structure, and handle schemas as they change and grow.
Moreover, you can use CLAIRE to augment your solution with machine learning. For example, to automatically detect and generate schemas. This is particularly beneficial because it allows you to discover and rectify data (and schema) drift. Informatica also provides a ready-made integration pipeline for data science and machine learning which helps you apply machine learning and AI models to your streaming flows.
In addition, Informatica is keenly aware of the need to govern your streaming data, and Informatica's robust suite of data governance products is available to do so. This includes data cataloging, preparation, discovery, lineage, and visualization, all designed with self-service and collaboration in mind. Security features like masking, authentication, and access control are also available. In addition, real-time job monitoring, analytics, and visualization are provided via the Operational Insights service.
What Are Some Examples of Real-World Streaming Analytics Use Cases?
Use cases of streaming analytics can be found across a variety of industries. Here are a few examples:
Manufacturing: Many manufacturers embed intelligent sensors in devices throughout their production line and supply chain. Analyzing the data from these sensors in real-time allows a manufacturer to spot problems and correct them before a product leaves the production line. This improves production and efficiency of operations—and saves money. For more on Industrial IoT and IoT data management, read our reference article.
Cybersecurity: In cybersecurity, streaming analytics can instantly identify anomalous behavior and suspicious activities and flag them for immediate investigation. So, rather than remediating after a problem occurs, early action can be taken to minimize damage.
Hospitality: Hotels can use streaming analytics to monitor reservations in real time. For example, if a hotel notices that there’s high availability in the late afternoon, it could text or email special promotions to guests in that area to fill those empty rooms that night.
Retail: Streaming analytics helps retailers improve sales by combining customer real-time transaction records and historical spending patterns to generate real-time offer alerts.
Healthcare: Improve patient care by using streaming analytics to collect and process bedside monitor data for clinical researchers to understand and detect diseases more effectively.
Financial services: Prevent credit card frauds with streaming analytics by tracking customer transactions in real time and detecting anomalies to send SMS alerts for potentially fraudulent activities.
Telco: Improve customer experience and reduce customer churn by capturing and analyzing the network connectivity and proactively predicting any potential downtime to inform the customers via SMS or email.
Ovo, Indonesia's largest payment, rewards, and financial services platform, uses Informatica Data Engineering Streaming to stream data from millions of customers and process it in real time to deliver personalized campaigns with maximum impact.
University of New Orleans uses Informatica Cloud Mass Ingestion and Snowflake to democratize data among university employees. It created a centralized cloud data warehouse to enable more effective analytics and accelerate cloud migration at low latency to improve student recruitment, admission and retention.
Informatica Data Management Cloud offers highly capable and well-integrated stream processing as part of its overall data management functionality to ingest, process and analyze real-time streaming data improving operational efficiency, optimize cost, and provide faster insights.
Download the whitepaper: Change Data Capture for Real-Time Data Ingestion and Streaming Analytics.