Introduction to Real-Time Analytics on Google Cloud
Ingest, process, and analyze event streams in real-time. Google Cloud's real-time analytics solutions make data more streamlined, useful, and accessible as soon as it's generated.
Benefits of Real-Time Analytics on Google Cloud
Creating real value from real-time Insights
Ingest, process, and analyze real-time event streams and take action with business impact on perishable, high-value information.
Eliminate operational complexity
Leverage a fully managed streaming infrastructure that automatically scales to address variable data volumes, performance throttling, and resource provisioning.
Get the Best of Google Cloud
Access native integrations with Vertex AI Workbench, BigQuery, and other Google Cloud services to develop smart solutions quickly and reliably.
Key Features of Google’s Real-Time Analytics Services
Adopt Simple Ingestion for Complex Events
Import and analyze hundreds of millions of events per second from applications or devices virtually anywhere in the world with Pub/Sub. Stream millions of events per second directly into your data warehouse for SQL-based analysis with BigQuery's streaming API. Or copy data from a relational database directly into BigQuery on a serverless platform with Datastream.
Unify Stream and Batch Processing without Lock-in
Unify batch and stream data analytics with equal ease and create consistent data paths with Dataflow. Dataflow ensures single processing, making your streaming pipelines more reliable and consistent for mission-critical applications. Data engineers can reuse code through Dataflow's open-source SDK, Apache Beam, which provides pipeline portability for hybrid or multi-cloud environments.
Keep your current Tools while Exploring Next-Generation AI
Connect, migrate, or scale on-premises Apache Kafka and Apache Spark-based solutions through Confluent Cloud and Dataproc. Combined with the Data Fusion GUI, data analysts and engineers can create streaming pipelines with just a few clicks. Integrate Google's Vertex AI Workbench into your flow analytics for real-time customization, anomaly detection, and predictive maintenance scenarios.
Streaming Analytics Services from Google Cloud
Simple, Reliable staging location for large-scale ingestion of streaming data originating anywhere in the world into BigQuery, data lakes, or operational databases.
Data processing service relies on the open source Apache Beam SDK to transform and enrich stream and batch data with equal reliability for the fast, serverless, unified stream, batch data processing, and saving costs.
BigQuery is a cost-effective, serverless enterprise data warehouse that works in multiple clouds and scales with your data to instantly import and analyze millions of rows of data and generate dashboards. Real-time view for big-picture insights.
A seamless replication of relational databases directly into BigQuery, enabling near-real-time insights into operational data.
Google Cloud Real-Time Analytics Solutions for Enterprises to Gain Faster Insights
Organizations get better organized, useful, and accessible data with Google Cloud stream analytics solutions that make data ingestion, processing, and analysis possible. I am sharing next the two examples on the Following:
Example - 1
Challenge: Digital transformation struggling with siloed systems
Renault wants to use industry data from up to 40 locations around the world to make data-driven business decisions and create new opportunities. However, many of their initiatives have been shut down, with data processing disconnected.
Solution: Design and build a new cloud architecture
Renault has launched a program called Industrial Data Management 4.0 (IDM 4.0) to bring together all these and upcoming initiatives and, above all, to design and build a single data platform and an archive of all Renault industrial data. The solution includes IoT connectors, sensors, BigQuery, Dataproc, and Dataflow.
Outcome: Deploy new opportunities and democratize data access.
The team has also succeeded in exposing data in a secure and controlled way to data scientists, business teams, or any application. Renault has leveraged this unified data to improve manufacturing, engineering, and supply chain processes. These new processes have connected more than 4,900 industrial devices through Renault's internal data collection solution, transmitting more than 1 billion messages/day.
Example - 2
Challenge: Slow on-premise SAP environment
The company wants to create so-called interconnected retail that allows customers to shop in-store, online, or both. The company's SAP environment needs to be more flexible to support this strategy. Their data warehouse needs to be upgraded to handle and analyze growing and increasingly diverse data sets.
Solution: Migrate SAP environment to Google Cloud
The company migrated its SAP applications, including S/4HANA, General Ledger, e-commerce system, customer activity repository (CAR), Enterprise Data Warehouse, and more. To Google Cloud. Business analysts now use BigQuery ML to machine learning directly on BigQuery business data and use AutoML to determine the best predictive model.
Outcome: Maximize data insights to support the Customer Experience
The company leverages Google Cloud Analytics to create the most efficient supply chain in the industry, including stronger demand forecasting, supplier delivery times, estimated delivery times, and more, while maintaining better security than before. The company now has the speed, scale, and flexibility to handle spikes in operations while fully delivering to its customers.
Real-time analytics collects and analyzes generated data so that organizations can make informed decisions quickly. With the advent of cloud computing, it's easier than ever to implement real-time analytics solutions that scale as your business grows. Leverage fully managed Google Cloud services that automatically scale to address variable data volumes, performance throttling, and resource provisioning. It allows businesses to understand their data and instantly make informed decisions in real time.