Google Cloud Services for Real-Time Analytics

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Overview of 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.

Advantages 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

You can access native integrations with Vertex AI Workbench, BigQuery, and other Google Cloud services to develop smart solutions quickly and reliably.

Key Features of Google Cloud Real-Time Analytics

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

With Dataflow, you can easily unify batch and stream data analytics and create consistent data paths. 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.

Google Cloud Streaming Analytics Services

Pub/Hub

Simple, Reliable staging location for large-scale ingestion of streaming data originating anywhere in the world into BigQuery, data lakes, or operational databases.

Dataflow

Data processing service relies on the open source Apache Beam SDK to transform and enrich stream and batch data with equal reliability for fast, serverless, unified stream, batch data processing, and saving costs.

BigQuery

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.

Datastream

A seamless replication of relational databases directly into BigQuery, enabling near-real-time insights into operational data.

Real-Time Analytics Solutions for Faster Enterprise 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 the next 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 securely and controlled 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, which allows customers to shop in-store, online, or both. To support this strategy, the company's SAP environment needs to be more flexible. The company's 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 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. This includes 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.

Efficiently integrate analytics to gain insights to make more accurate predictions. Real-Time Analytics Strategy for Enterprises

Conclusion

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.