AWS Data Pipeline vs Google Cloud Dataflow

Overview

AWS Data Pipeline

Stacks94

Followers398

Votes1

Google Cloud Dataflow

Stacks219

Followers497

Votes19

AWS Data Pipeline vs Google Cloud Dataflow: What are the differences?

AWS Data Pipeline and Google Cloud Dataflow are cloud-based data processing services offering different approaches to data orchestration and transformation. Let's explore the key differences between the two platforms.

Processing Model and Workflow: AWS Data Pipeline follows a batch processing model and uses a visual workflow editor to create pipelines. Google Cloud Dataflow supports both batch and stream processing models and uses a programming model based on Apache Beam.
Ecosystem and Integration: AWS Data Pipeline integrates well with various AWS services such as S3, DynamoDB, Redshift, and EMR, allowing seamless data movement within the AWS ecosystem. Google Cloud Dataflow is tightly integrated with other Google Cloud services like BigQuery, Pub/Sub, and Cloud Storage, offering a cohesive data processing and analytics solution within the Google Cloud Platform.
Scalability and Elasticity: AWS Data Pipeline offers automatic scaling and elasticity, allowing the pipelines to handle varying workloads by automatically adjusting the compute resources. Google Cloud Dataflow offers automatic scaling and elasticity as well, but it leverages the power of Google Cloud Dataflow Shuffle service to optimize data shuffling and achieve higher throughput.
Fault Tolerance and Recovery: AWS Data Pipeline provides fault tolerance through retry mechanisms and failure handling capabilities. It can also recover and resume activities from the point of failure. Google Cloud Dataflow ensures fault tolerance with its automatic retries and provides robust error handling capability. It also supports checkpointing and allows resuming pipelines from failure points.
Monitoring and Management: AWS Data Pipeline offers comprehensive monitoring, logging, and alerting features through AWS CloudTrail, Amazon CloudWatch, and Amazon SNS. It provides detailed execution status and performance metrics. Google Cloud Dataflow provides real-time monitoring and diagnostic information through Stackdriver Monitoring and Logging, allowing users to track job progress, success rates, and resource utilization.
Pricing Model: AWS Data Pipeline has a flexible pricing model, charging based on pipeline activation and resource usage, with different rates for on-demand and scheduled pipelines, as well as for data transfer and storage. Google Cloud Dataflow has a simplified pricing model that charges based on the processing units consumed per second, providing a predictable and transparent billing experience.

In summary, AWS Data Pipeline is more tightly integrated with the AWS ecosystem and follows a visual workflow editor approach, while Google Cloud Dataflow offers a programming model based on Apache Beam and leverages the power of Google Cloud services for data processing and analytics. Both platforms provide scalability, fault tolerance, monitoring, and different pricing models.

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CLI (Node.js)

Manual

Detailed Comparison

AWS Data Pipeline	Google Cloud Dataflow
AWS Data Pipeline is a web service that provides a simple management system for data-driven workflows. Using AWS Data Pipeline, you define a pipeline composed of the “data sources” that contain your data, the “activities” or business logic such as EMR jobs or SQL queries, and the “schedule” on which your business logic executes. For example, you could define a job that, every hour, runs an Amazon Elastic MapReduce (Amazon EMR)–based analysis on that hour’s Amazon Simple Storage Service (Amazon S3) log data, loads the results into a relational database for future lookup, and then automatically sends you a daily summary email.	Google Cloud Dataflow is a unified programming model and a managed service for developing and executing a wide range of data processing patterns including ETL, batch computation, and continuous computation. Cloud Dataflow frees you from operational tasks like resource management and performance optimization.
You can find (and use) a variety of popular AWS Data Pipeline tasks in the AWS Management Console’s template section.;Hourly analysis of Amazon S3‐based log data;Daily replication of AmazonDynamoDB data to Amazon S3;Periodic replication of on-premise JDBC database tables into RDS	Fully managed; Combines batch and streaming with a single API; High performance with automatic workload rebalancing Open source SDK;
Statistics
Stacks 94	Stacks 219
Followers 398	Followers 497
Votes 1	Votes 19
Pros & Cons
Pros 1 Easy to create DAG and execute it	Pros 7 Unified batch and stream processing 5 Autoscaling 4 Fully managed 3 Throughput Transparency

What are some alternatives to AWS Data Pipeline, Google Cloud Dataflow?

Amazon Kinesis

Amazon Kinesis can collect and process hundreds of gigabytes of data per second from hundreds of thousands of sources, allowing you to easily write applications that process information in real-time, from sources such as web site click-streams, marketing and financial information, manufacturing instrumentation and social media, and operational logs and metering data.

AWS Snowball Edge

AWS Snowball Edge is a 100TB data transfer device with on-board storage and compute capabilities. You can use Snowball Edge to move large amounts of data into and out of AWS, as a temporary storage tier for large local datasets, or to support local workloads in remote or offline locations.

Requests

It is an elegant and simple HTTP library for Python, built for human beings. It allows you to send HTTP/1.1 requests extremely easily. There’s no need to manually add query strings to your URLs, or to form-encode your POST data.

Amazon Kinesis Firehose

Amazon Kinesis Firehose is the easiest way to load streaming data into AWS. It can capture and automatically load streaming data into Amazon S3 and Amazon Redshift, enabling near real-time analytics with existing business intelligence tools and dashboards you’re already using today.

NPOI

It is a .NET library that can read/write Office formats without Microsoft Office installed. No COM+, no interop.

HTTP/2

It's focus is on performance; specifically, end-user perceived latency, network and server resource usage.

Embulk

It is an open-source bulk data loader that helps data transfer between various databases, storages, file formats, and cloud services.

Google BigQuery Data Transfer Service

BigQuery Data Transfer Service lets you focus your efforts on analyzing your data. You can setup a data transfer with a few clicks. Your analytics team can lay the foundation for a data warehouse without writing a single line of code.

PieSync

A cloud-based solution engineered to fill the gaps between cloud applications. The software utilizes Intelligent 2-way Contact Sync technology to sync contacts in real-time between your favorite CRM and marketing apps.

Resilio

It offers the industry leading data synchronization tool. Trusted by millions of users and thousands of companies across the globe. Resilient, fast and scalable p2p file sync software for enterprises and individuals.

Related Comparisons

AWS Data Pipeline vs Google Cloud Dataflow: What are the differences?

Processing Model and Workflow: AWS Data Pipeline follows a batch processing model and uses a visual workflow editor to create pipelines. Google Cloud Dataflow supports both batch and stream processing models and uses a programming model based on Apache Beam.
Ecosystem and Integration: AWS Data Pipeline integrates well with various AWS services such as S3, DynamoDB, Redshift, and EMR, allowing seamless data movement within the AWS ecosystem. Google Cloud Dataflow is tightly integrated with other Google Cloud services like BigQuery, Pub/Sub, and Cloud Storage, offering a cohesive data processing and analytics solution within the Google Cloud Platform.
Scalability and Elasticity: AWS Data Pipeline offers automatic scaling and elasticity, allowing the pipelines to handle varying workloads by automatically adjusting the compute resources. Google Cloud Dataflow offers automatic scaling and elasticity as well, but it leverages the power of Google Cloud Dataflow Shuffle service to optimize data shuffling and achieve higher throughput.
Fault Tolerance and Recovery: AWS Data Pipeline provides fault tolerance through retry mechanisms and failure handling capabilities. It can also recover and resume activities from the point of failure. Google Cloud Dataflow ensures fault tolerance with its automatic retries and provides robust error handling capability. It also supports checkpointing and allows resuming pipelines from failure points.
Monitoring and Management: AWS Data Pipeline offers comprehensive monitoring, logging, and alerting features through AWS CloudTrail, Amazon CloudWatch, and Amazon SNS. It provides detailed execution status and performance metrics. Google Cloud Dataflow provides real-time monitoring and diagnostic information through Stackdriver Monitoring and Logging, allowing users to track job progress, success rates, and resource utilization.
Pricing Model: AWS Data Pipeline has a flexible pricing model, charging based on pipeline activation and resource usage, with different rates for on-demand and scheduled pipelines, as well as for data transfer and storage. Google Cloud Dataflow has a simplified pricing model that charges based on the processing units consumed per second, providing a predictable and transparent billing experience.

AWS Data Pipeline vs Google Cloud Dataflow

Overview