In recent years, companies and large organizations have been facing the growing demand for high-performance analytics. The current market requires fast and reliable results. Therefore, digital transformation and business analytics must be a priority on the agenda of many organizations. Do you know what Data Flow is? It is a key component in this digital transformation, keep reading to learn more.
What is Data Flow?
Data Flow is a serverless, cloud-based platform with a rich user interface. It allows developers and Spark data scientists to create, edit, and run Spark jobs at scale without the need for clusters, operations teams, or highly specialized Spark knowledge. Being serverless means there's no infrastructure to deploy or manage. It is fully controlled by REST APIs, enabling easy integration with applications or workflows.
Data Flow is also a collection of entities or tables created and managed within workspaces on the Power BI service, allowing users to add and edit these entities. In simpler terms, it's like running Power Query in the cloud, independent of the dataset and Power BI report, while also storing data in CDM (Common Data Model) within Azure Data Lake storage.
Once the Data Flow is created, we can connect to it via Power BI Desktop to create datasets, reports, dashboards, and applications based on this integrated data, generating insights.
What is it about?
It is about giving business users the ability to connect directly to their frequently used data sources, allowing not only the extraction of information from them but also linking it to other systems. More importantly, it provides the ability to transform, clean, and manipulate data without requiring a desktop tool to perform this task.
One of the key pillars behind Data Flow is also the connection to Azure Data Lake Gen2 with storage capabilities. This feature is available using either a Power BI PRO account or the Premium capacity of Power BI.
What Are the Features of Data Flow?
Auto scaling and Dynamic Load Balancing
Minimizes the latency of processing flows, increases resource utilization, and reduces data processing costs by auto-scaling resources based on data. The system performs automatic partitioning of data inputs, which are constantly leveled to balance worker resource usage and reduce the impact of "hot keys" on processing flow performance.
Flexible Scheduling and Pricing for Batch Processing
Some tasks can be scheduled more flexibly, for example, to run overnight. In these cases, batch processing costs less if you use FlexRS, the flexible resource scheduling. Flexible tasks are queued with the guarantee that they will execute within a maximum of six hours.
Real-Time AI Patterns Ready to Use
The real-time AI features provided by Data Flow are enabled through ready-to-use patterns, providing a system capable of reacting instantly to large amounts of events with almost human-like intelligence. Clients can build intelligent solutions such as predictive analytics, anomaly detection, real-time personalization, and other advanced analytics uses.
When Should We Use Data Flow?
- When we need to create a reusable logic process, i.e., one that can be used by multiple datasets without needing to redo it.
- To centralize a single data source, enabling different users to connect to the same flows and consolidate the same information while being able to assign standard business definitions, allowing you to create organized tables that can also work with other services and products in Power Platform.
- If you want to work with large volumes of data.
What Are Its Main Benefits?
- Fully managed data processing service
- Automatic provisioning and management of processing resources
- Automatic horizontal scaling of worker resources to maximize resource usage
- Community-driven innovation in OSS with the Apache Beam SDK
- Reliable and consistent "exactly once" processing
What Can Data Flow Enable Us to Do?
- Connect to Apache Spark data sources.
- Create reusable Apache Spark applications.
- Start Apache Spark jobs in seconds.
- Create Apache Spark applications using SQL, Python, Java, Scala, or spark-submit.
- Manage all Apache Spark applications from a single platform.
- Process data either in the cloud or on-premises in your data center.
- Create big data building blocks that can be easily assembled into advanced big data applications.
What Practical Uses Can Data Flow Have?
Do You Use Streaming?
Thanks to Google’s streaming analytics, the data is more organized and useful. You can also access the data as soon as it is generated. Our streaming solution relies on Dataflow, Pub/Sub, and BigQuery. It provisions the resources necessary to ingest, process, and analyze variable volumes of real-time data to provide useful business insights instantly. Besides reducing complexity, this abstract provisioning makes it easier for both analysts and data engineers to perform real-time analytics.
Real-Time Artificial Intelligence?
Dataflow sends streaming events to Google Cloud’s AI Platform and TensorFlow Extended (TFX) solutions to enable predictive analytics, fraud detection, real-time personalization, and other advanced analytics uses.
TFX uses Dataflow and Apache Beam as the distributed data processing engine for various aspects of the machine learning lifecycle, and all are compatible with continuous integration and delivery (CI/CD) for machine learning through Kubeflow processing flows.
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Integration with Notebooks?
Create processing flows iteratively from scratch with Vertex AI Notebooks and deploy them with the Data Flow executor. To build step-by-step Apache Beam processing flows, inspect flow charts in a read-evaluate-print-loop (REPL) workflow.
With Notebooks, available on Google’s Vertex AI, you can write processing flows in an intuitive environment thanks to cutting-edge data science and machine learning frameworks.
Real-Time Data?
Synchronize or reliably replicate data with minimal latency across multiple heterogeneous data sources to improve real-time analytics. Extendable Dataflow templates integrate with Datastream to replicate data from Cloud Storage into BigQuery, PostgreSQL, or Cloud Spanner. The Apache Beam Debezium connector is an open-source option to ingest data changes from MySQL, PostgreSQL, SQL Server, and Db2.
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