AWS Data Pipeline: Platform for Solving ETL or ELT Headaches
Realizing fully automated, zero administration data pipelines for a data warehouse or data lake
AWS Data Pipeline (or Amazon Data Pipeline) is “infrastructure-as-a-service” web services that support automating the transport and transformation of data.
Pipelines reflect an ETL process that allows you to obtain more value from your data across multiple sources via data extraction and data transformation downstream to Amazon Web Services such as Amazon RDS, Amazon Athena, and Amazon Redshift.
This ETL pipeline process reflects an example of an innovative and cost-effective pipeline architecture is highlighted by teams building serverless business intelligence stacks with Apache Parquet, Tableau, and Amazon Athena.
What Is the AWS Data Pipeline?
- Deployed within the distributed, highly available AWS infrastructure
- Provides a drag-and-drop console within the AWS interface
- Supports scheduling, dependency tracking, and error handling
- Distribute work to one machine or many
- Billed based on use levels, at a monthly rate
- Select the computing resources that execute your ETL or ELT pipeline logic
Why Pipelines Are Important
Pipelines are critical to the business. Why? Take a moment to think about all those systems you or your team use every day to connect, communicate, engage, manage, and delight your customers. These platforms cover things like email, social, loyalty, advertising, mobile, web, and a host of others. All of that information resides in data silos. All too often this results in manual data wrangling as teams attempt to break down those silos.
These data extraction and data transformation processes allow you to move and process data that was previously locked up in those remote data silos.
A pipeline solves the logistics between data sources (or systems where data resides) and data consumers or those who need access to data to undertake further processing, visualizations, transformations, routing, reporting or statistical models.
Here is an example of a pipeline to a data lake and Oracle Cloud:
When establishing pipelines, you are attempting to reduce friction with ETL or ELT workflow, resource availability, dependencies, transient failures or timeouts in individual tasks, or other elements. Creating a pipeline, including the use of the AWS product, solves complex data processing workloads need to close the gap between data sources and data consumers.
Getting Started With AWS Data Pipelines
Access to the service occurs via the AWS Management Console, the AWS command-line interface or service APIs. You can find details about AWS pricing and AWS documentation. Amazon provides a series of AWS tutorials to help kick-start your efforts:
- Process Data Using Amazon EMR with Hadoop Streaming
- Import and Export DynamoDB Data
- Copy CSV Data Between Amazon S3 Buckets
- Export MySQL Data to Amazon S3
- Copy Data to Amazon Redshift
No Need To Stitch ETL Tools Together Or Hire Data Engineers — Pre-built Data Pipeline Platform To The Rescue!
While Amazon has done a great job of providing a collection of tools, templates, and frameworks with this service, it still requires a certain level of developer/engineering expertise to create a data extraction process. The code must be written, tested, deployed, and managed within the service. This might not be viable for some teams who lack that expertise. If you can’t or don’t want to build custom pipelines, what options do you have?
For teams without that expertise and do not want to stitch ETL tools or ETL processes together, we suggest an alternative approach. Openbridge eliminates the time-consuming engineering endeavor to create data processing pipelines. There is a fast, cost-effective catalog of pre-built data ETL processes for social media, advertising, support, e-commerce, analytics, and other marketing technology categories brought for you by the Openbridge team.
The catalog includes 600+ data sources like Google Analytics 360, DoubleClick, Instagram, YouTube, Adobe Analytics, Facebook, Salesforce, Marketo, Zendesk, HubSpot, and many more.
Browse and pick your data sources to streamline data to cloud data warehouse solutions like Amazon Redshift, Amazon Redshift Spectrum, Amazon Athena, Google BigQuery, and Panoply.io.
You can also discover tools for data analysis, visualization, reporting, and analysis like Tableau Software, Mode Analytics, Periscope Data, Redash, QlikView, Chartio, Looker, and many others.
Summary
Pipelines are a critical part of the roadmap, architecture, and operations of anyone looking to use data as a strategic asset. Given the complexity and diversity of data sources, this can be a challenge for even the most sophisticated teams of product managers, account managers, solutions architects, and support engineers.
If data is a first-class organizational asset, then pipelines are needed to provide the connections to data spread across different systems.
This approach ensures that you are not unnecessarily wasting the time of valuable human resources like analysts, engineers, or data scientists undertaking manual data munging efforts for ETL or ELT development.
We have launched a code-free, zero-admin, fully automated data pipeline that automates database, table creation, Parquet file conversion, partitioning, and more.
Get started with Amazon Redshift, Redshift Spectrum or Amazon Athena for free!
DWant to discuss how pipelines can help your organization? Need a platform and team of experts to kickstart your data and analytics efforts? We can help! Getting traction adopting new technologies, especially working in different and unfamiliar ways, can be a roadblock for success in a self-service only world. If you want to discuss a proof-of-concept, pilot, project, or any other effort, the Openbridge platform and team of data experts are ready to help.
Reach out to us at hello@openbridge.com. Prefer to talk to someone? Set up a call with our team of data experts.
Visit us at www.openbridge.com to learn how we are helping other companies with their data efforts.
