Every day, Amazon devices process and analyze billions of transactions from global shipping, inventory, capacity, supply, sales, marketing, producers, and customer service teams. This data is used in procuring devices’ inventory to meet Amazon customers’ demands. With data volumes exhibiting a double-digit percentage growth rate year on year and the COVID pandemic disrupting global logistics in 2021, it became more critical to scale and generate near-real-time data.
This post shows you how we migrated to a serverless data lake built on AWS that consumes data automatically from multiple sources and different formats. Furthermore, it created further opportunities for our data scientists and engineers to use AI and machine learning (ML) services to continuously feed and analyze data.
Challenges and design concerns
Our legacy architecture primarily used Amazon Elastic Compute Cloud (Amazon EC2) to extract the data from various internal heterogeneous data sources and REST APIs with the combination of Amazon Simple Storage Service (Amazon S3) to load the data and Amazon Redshift for further analysis and generating the purchase orders.
We found this approach resulted in a few deficiencies and therefore drove improvements in the following areas:
Developer velocity – Due to the lack of unification and discovery of schema, which are primary reasons for runtime failures, developers often spent time dealing with operational and maintenance issues.
Scalability – Most of these datasets are shared across the globe. Therefore, we must meet the scaling limits while querying the data.
Minimal infrastructure maintenance – The current process spans multiple computes depending on the data source. Therefore, reducing infrastructure maintenance is critical.
Responsiveness to data source changes – Our current system gets data from various heterogeneous data stores and services. Any updates to those services takes months of developer cycles. The response times for these data sources are critical to our key stakeholders. Therefore, we must take a data-driven approach to select a high-performance architecture.
Storage and redundancy – Due to the heterogeneous data stores and models, it was challenging to store the different datasets from various business stakeholder teams. Therefore, having versioning along with incremental and differential data to compare will provide a remarkable ability to generate more optimized plans
Fugitive and accessibility – Due to the volatile nature of logistics, a few business stakeholder teams have a requirement to analyze the data on demand and generate the near-real-time optimal plan for the purchase orders. This introduces the need for both polling and pushing the data to access and analyze in near-real time.
Based on these requirements, we changed strategies and started analyzing each issue to identify the solution. Architecturally, we chose a serverless model, and the data lake architecture action line refers to all the architectural gaps and challenging features we determined were part of the improvements. From an operational standpoint, we designed a new shared responsibility model for data ingestion using AWS Glue instead of internal services (REST APIs) designed on Amazon EC2 to extract the data. We also used AWS Lambda for data processing. Then we chose Amazon Athena as our query service. To further optimize and improve the developer velocity for our data consumers, we added Amazon DynamoDB as a metadata store for different data sources landing in the data lake. These two decisions drove every design and implementation decision we made.
The following diagram illustrates the architecture
In the following sections, we look at each component in the architecture in more detail as we move through the process flow.
AWS Glue for ETL
To meet customer demand while supporting the scale of new businesses’ data sources, it was critical for us to have a high degree of agility, scalability, and responsiveness in querying various data sources.
AWS Glue is a serverless data integration service that makes it easy for analytics users to discover, prepare, move, and integrate data from multiple sources. You can use it for analytics, ML, and application development. It also includes additional productivity and DataOps tooling for authoring, running jobs, and implementing business workflows.
With AWS Glue, you can discover and connect to more than 70 diverse data sources and manage your data in a centralized data catalog. You can visually create, run, and monitor extract, transform, and load (ETL) pipelines to load data into your data lakes. Also, you can immediately search and query cataloged data using Athena, Amazon EMR, and Amazon Redshift Spectrum.
AWS Glue made it easy for us to connect to the data in various data stores, edit and clean the data as needed, and load the data into an AWS-provisioned store for a unified view. AWS Glue jobs can be scheduled or called on demand to extract data from the client’s resource and from the data lake.
Some responsibilities of these jobs are as follows:
Extracting and converting a source entity to data entity
Enrich the data to contain year, month, and day for better cataloging and include a snapshot ID for better querying
Perform input validation and path generation for Amazon S3
Associate the accredited metadata based on the source system
Querying REST APIs from internal services is one of our core challenges, and considering the minimal infrastructure, we wanted to use them in this project. AWS Glue connectors assisted us in adhering to the requirement and goal. To query data from REST APIs and other data sources, we used PySpark and JDBC modules.
AWS Glue supports a wide variety of connection types. For more details, refer to Connection Types and Options for ETL in AWS Glue.
S3 bucket as landing zone
We used an S3 bucket as the immediate landing zone of the extracted data, which is further processed and optimized.
Lambda as AWS Glue ETL Trigger
We enabled S3 event notifications on the S3 bucket to trigger Lambda, which further partitions our data. The data is partitioned on InputDataSetName, Year, Month, and Date. Any query processor running on top of this data will scan only a subset of data for better cost and performance optimization. Our data can be stored in various formats, such as CSV, JSON, and Parquet.
The raw data isn’t ideal for most of our use cases to generate the optimal plan because it often has duplicates or incorrect data types. Most importantly, the data is in multiple formats, but we quickly modified the data and observed significant query performance gains from using the Parquet format. Here, we used one of the performance tips in Top 10 performance tuning tips for Amazon Athena.
AWS Glue jobs for ETL
We wanted better data segregation and accessibility, so we chose to have a different S3 bucket to improve performance further. We used the same AWS Glue jobs to further transform and load the data into the required S3 bucket and a portion of extracted metadata into DynamoDB.
DynamoDB as metadata store
Now that we have the data, various business stakeholders further consume it. This leaves us with two questions: which source data resides on the data lake and what version. We chose DynamoDB as our metadata store, which provides the latest details to the consumers to query the data effectively. Every dataset in our system is uniquely identified by snapshot ID, which we can search from our metadata store. Clients access this data store with an API’s.
Amazon S3 as data lake
For better data quality, we extracted the enriched data into another S3 bucket with the same AWS Glue job.
AWS Glue Crawler
Crawlers are the “secret sauce” that enables us to be responsive to schema changes. Throughout the process, we chose to make each step as schema-agnostic as possible, which allows any schema changes to flow through until they reach AWS Glue. With a crawler, we could maintain the agnostic changes happening to the schema. This helped us automatically crawl the data from Amazon S3 and generate the schema and tables.
AWS Glue Data Catalog
The Data Catalog helped us maintain the catalog as an index to the data’s location, schema, and runtime metrics in Amazon S3. Information in the Data Catalog is stored as metadata tables, where each table specifies a single data store.
Athena for SQL queries
Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries you run. We considered operational stability and increasing developer velocity as our key improvement factors.
We further optimized the process to query Athena so that users can plug in the values and the queries to get data out of Athena by creating the following:
An AWS Cloud Development Kit (AWS CDK) template to create Athena infrastructure and AWS Identity and Access Management (IAM) roles to access data lake S3 buckets and the Data Catalog from any account
A library so that client can provide an IAM role, query, data format, and output location to start an Athena query and get the status and result of the query run in the bucket of their choice.
To query Athena is a two-step process:
StartQueryExecution – This starts the query run and gets the run ID. Users can provide the output location where the output of the query will be stored.
GetQueryExecution – This gets the query status because the run is asynchronous. When successful, you can query the output in an S3 file or via API.
The helper method for starting the query run and getting the result would be in the library.
Data lake metadata service
This service is custom developed and interacts with DynamoDB to get the metadata (dataset name, snapshot ID, partition string, timestamp, and S3 link of the data) in the form of a REST API. When the schema is discovered, clients use Athena as their query processor to query the data.
Because all datasets have a snapshot ID are partitioned, the join query doesn’t result in a full table scan but only a partition scan on Amazon S3. We used Athena as our query processor because of its ease in not managing our query infrastructure. Later, if we feel we need something more, we can use either Redshift Spectrum or Amazon EMR.
Amazon Devices teams discovered significant value by moving to a data lake architecture using AWS Glue, which enabled multiple global business stakeholders to ingest data in more productive ways. This enabled the teams to generate the optimal plan to place purchase orders for devices by analyzing the different datasets in near-real time with appropriate business logic to solve the problems of the supply chain, demand, and forecast.
From an operational perspective, the investment has already started to pay off:
It standardized our ingestion, storage, and retrieval mechanisms, saving onboarding time. Before the implementation of this system, one dataset took 1 month to onboard. Due to our new architecture, we were able to onboard 15 new datasets in less than 2 months, which improved our agility by 70%.
It removed scaling bottlenecks, creating a homogeneous system that can quickly scale to thousands of runs.
The solution added schema and data quality validation before accepting any inputs and rejecting them if data quality violations are discovered.
It made it easy to retrieve datasets while supporting future simulations and back tester use cases requiring versioned inputs. This will make launching and testing models simpler.
The solution created a common infrastructure that can be easily extended to other teams across DIAL having similar issues with data ingestion, storage, and retrieval use cases.
Our operating costs have fallen by almost 90%.
This data lake can be accessed efficiently by our data scientists and engineers to perform other analytics and have a predictive approach as a future opportunity to generate accurate plans for the purchase orders.
The steps in this post can help you plan to build a similar modern data strategy using AWS-managed services to ingest data from different sources, automatically create metadata catalogs, share data seamlessly between the data lake and data warehouse, and create alerts in the event of an orchestrated data workflow failure.
About the authors
Avinash Kolluri is a Senior Solutions Architect at AWS. He works across Amazon Alexa and Devices to architect and design modern distributed solutions. His passion is to build cost-effective and highly scalable solutions on AWS. In his spare time, he enjoys cooking fusion recipes and traveling.
Vipul Verma is a Sr.Software Engineer at Amazon.com. He has been with Amazon since 2015,solving real-world challenges through technology that directly impact and improve the life of Amazon customers. In his spare time, he enjoys hiking.