Exam Professional Cloud Architect topic 1 question 12 discussion

All four options can accomplish what the question asks, in regards to batching and streaming processes. "A" is for Apache Spark and Hadoop, a juggernaut in speed of data processing. "B" is Google's best attempt at TIBCO, Ab Initio, and other processing technology, built explicity for visualizing batch operations and streams without through various labeled circuit boards. "C" and "D" are used within "A" and "B" and would require more work and higher risk. I'd guess Google wants you to select "B"

1. Unified Batch and Stream Processing: Dataflow is a fully managed service designed for both batch and stream data processing. This makes it ideal for your company's needs, as they require both hourly batch jobs and live stream processing. 2. No Existing Code: Dataflow provides a unified programming model and SDKs (Java, Python) for building data pipelines, which is beneficial since your company doesn't have existing code and needs to develop new solutions. 3. Serverless and Scalable: Dataflow is serverless, meaning you don't need to manage infrastructure. It automatically scales resources based on the workload, ensuring efficient processing of both batch and stream data. 4. Cost-Effective: Dataflow's autoscaling and pay-per-use model optimize costs by only utilizing resources when needed.

DataFlow is a managed service which processes stream and batch data. So it prefectly fits the need

B. Google Cloud Dataflow Explanation: Unified Processing: Google Cloud Dataflow is designed to handle both batch and stream processing in a unified manner. This means you can process data as it arrives (stream processing) and also perform scheduled batch jobs efficiently. Serverless and Scalable: Dataflow is serverless, which means you don’t have to worry about managing the underlying infrastructure. It automatically scales to handle varying workloads, making it ideal for optimizing operations based on live data streams and scheduled jobs. Integration with Other Google Cloud Services: Dataflow integrates well with other Google Cloud services, such as Google Cloud Storage, BigQuery, and Pub/Sub. This makes it easier to build a comprehensive data pipeline that can analyze data streams effectively. Flexible SDKs: Dataflow supports popular programming languages like Java and Python, allowing your team to write custom processing logic as needed.

Choose B

B is correct

chatGPT answers: B. Google Cloud Dataflow Google Cloud Dataflow is a fully managed service for stream and batch data processing. It is built on Apache Beam and provides a unified programming model, making it an ideal choice for scenarios where both batch and stream data processing are required. Dataflow simplifies the complexities of data parallel processing, allowing for easy development and maintenance of data processing pipelines. It integrates well with other Google Cloud services, like BigQuery for analytics and Cloud Storage for storing data, providing a comprehensive solution for real-time and batch data processing needs.

The word analysis throws me off. Wonder if the question is just written incorrectly here? I'd say Dataflow is a key tool to enable the processing of the data to be able to do the analysis but feels like the final analysis should be in a database.

B is the answer

A is a managed Hadoop and Spark service. C and D are mostly for petabyte kinds of data. So remains B (suitable for ETL jobs)

To analyze a data stream and optimize operations, your company could consider using Google Cloud Dataflow, which is a fully-managed, cloud-native data processing service that can handle both batch and stream processing. Google Cloud Dataflow is designed to handle large volumes of data and can scale up or down automatically to meet the needs of the workload. It provides a number of pre-built connectors and integrations that make it easy to ingest data from a variety of sources, and it offers a range of processing options, including batch processing and stream processing, that can be used to analyze the data in real-time. Option A: Google Cloud Dataproc, option C: Google Container Engine with Bigtable, and option D: Google Compute Engine with Google BigQuery, while potentially useful for certain types of data processing, would not necessarily be well-suited to handle both batch and stream processing in the way that Google Cloud Dataflow can

answer is D for me the question is which tool for analyse data. Dataflow does not analyse data

ok for B

B is the right answer

B is correct

B. Google Cloud Dataflow

correct is B use data flow for stream and batch process