Written by Priya GeorgeContent Writer
By transforming raw data for actionable insights, data processing cements itself central to building data pipelines. Stream and batch data processing are the two main types of processing. Batch processing refers to typically homogenous data collected and processed in batches. Such processing operations are helpful for high-volume, repetitive entries that require minimal human interaction. Stream processing refers to when data is processed and transformed as it is ingested. As a result, streaming analytics has become essential for modern data analytics platforms.
Cloud platforms need to provide the tools to allow both forms of data processing to create comprehensive data pipelines. And with modern-day applications, it is ideal that data engineers spend time innovating data pipelines rather than managing routine processing operations. Recognizing this, Google Cloud Platform released Cloud Dataflow in 2019 to provide a unified processing platform with low latency, serverless, and highly cost-effective.
The key benefits of Cloud Dataflow service include:
Elimination of operational overhead for data engineering workloads
Low latency for building streaming data pipelines
Cost-optimized for sudden spikes in workload
Google Cloud Dataflow is instrumental in data processing as companies can structure and categorize their raw data for further analytics. However, manual processing is highly costly in time and resources. With the Google Cloud Dataflow service, developers have access to a fully managed service that allows streaming and batch data processing. In addition, it enables portability by using open-source Apache Beam libraries and reduces operational management by teams as it automates provisioning and cluster management. The key features of Dataflow are:
Access AI capabilities for predictive analytics and anomaly discovery
Flexible schedule and pay for batch processing
Autoscaling data resources
Raw data extracted from multiple sources are transformed into immutable parallel collections (PCollection) and transferred to a data sink in Google Cloud Storage. The data pipeline can be constructed with Apache SDK using Python and Java. The deployment and execution of this pipeline are referred to as a ‘Dataflow job.’ By separating compute and cloud storage and moving parts of pipeline execution away from worker VMs on Compute Engine, Google Cloud Dataflow ensures lower latency and easy autoscaling.
Creating Dataflow jobs is achieved through Google Cloud Console, GCloud Command Line Interface, or API. However, when it comes to building the pipelines themselves, there are three options:
You can monitor Dataflow jobs with metrics that measure problems at the step and worker levels. Dataflow ensures end-to-end encryption for rest and transit. In addition, you can limit public IPs engage VPC Service Controls and Customer Managed encryption keys.
Dataflow pricing is billed in per-second increments and depends on the job stream or batch data processing type. The price of the job depends on the worker VM configurations, although by scheduling batch processing, you can automate and save costs.
With managed services for batch and streaming data processing, Cloud Dataflow service sets itself apart from other cloud-native data services and continuously updates itself. For example, the new release of Google Cloud Dataflow fully supports Apache Beam SDK releases, unlike earlier when the two services were separated.
Want to learn more about how Google Cloud data tools and services can help grow your business? Please read our blog, where we unpack how practical data engineering can translate to meeting growth objectives.
Dataflow offers batch and stream data processing across cloud and non-cloud platforms without any vendor lock-in. This makes it a valuable data service across multiple industries. For instance, online retail can conduct data analytics at the point of sale and various forms of customer segmentation. With analytics being performed in real-time, the processing speed must be proportional, making Cloud Dataflow extremely valuable in modern-day cybersecurity, especially in the financial sector, where petabytes of data need to be analyzed to detect potential fraudulent attacks. IoT computing is where cloud-based Dataflow gets a chance to shine, especially for IoT applications within the healthcare and manufacturing industries. Large volumes of data are exported from IoT devices to be processed and analyzed on an off-site cloud-native application.
In recognition of the several capabilities and benefits, Forrester named Google Cloud Platform, the leader in streaming analytics for Q2,2021. In addition, cloud Dataflow was awarded 5/5 for the streaming analytics criteria, which included:
As partners of Google Cloud Platform, we have the certified resources necessary to provide the managed and consulting services needed for building custom end-to-end solutions for your enterprise. In addition, by collaborating with our data engineers and AI/ML experts, your organization can build advanced data pipelines that help guide organizational growth with data-driven insights.
Furthermore, our data engineers can extend client capabilities by:
Automating and optimizing data flow processes to achieve scalability
Cleaning the data for data analysts to build predictive models
Creating and maintaining ETL pipelines