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AWS Glue vs s3-lambda: What are the differences?
AWS Glue is Amazon's managed ETL service, while s3-lambda is a framework for deploying serverless AWS Lambda functions to process data stored in Amazon S3. Let's explore the key differences between them.
Data Transformation Capabilities: AWS Glue is a fully managed extract, transform, and load (ETL) service that allows easy data transformation and integration. It provides a graphical interface to create ETL jobs and supports various data formats, field mapping, and complex transformations. On the other hand, S3-Lambda is a serverless compute service that automatically triggers code when objects are added or modified in Amazon S3. While it supports data processing workflows using Lambda functions, it does not offer the comprehensive data transformation capabilities of Glue.
Data Catalog and Schema Discovery: AWS Glue includes a centralized metadata repository, known as the Data Catalog, which automatically discovers, catalogs, and tracks metadata changes in data sources. It enables schema discovery and automatically generates ETL scripts for data transformation. In contrast, S3-Lambda does not provide a built-in data catalog or schema discovery features. Developers would need to implement their own mechanisms for schema management and tracking metadata changes.
Job Orchestration and Scheduling: AWS Glue offers built-in job orchestration and scheduling features, allowing users to schedule, monitor, and manage dependencies between ETL jobs. Users can define triggers and workflows to control the execution of ETL tasks. In contrast, S3-Lambda is primarily a serverless compute service for processing individual S3 events. While it can be used to trigger code based on S3 events, it lacks the sophisticated job orchestration and scheduling capabilities provided by Glue.
Data Source Connectivity: AWS Glue provides native connectivity to a wide range of data sources, including relational databases, Amazon S3, DynamoDB, and more. It supports connecting to external data sources via JDBC and ODBC connectors. S3-Lambda, on the other hand, primarily focuses on processing data stored in Amazon S3 buckets. While it can interact with other AWS services like AWS Lambda, it does not have native support for various data sources like Glue.
Data Lineage and Impact Analysis: AWS Glue captures and records data lineage information, allowing users to track the flow of data across ETL jobs, transformations, and data sources. It provides visibility into the impact analysis of changes to data sources and helps ensure data accuracy and compliance. Conversely, S3-Lambda does not offer built-in capabilities for data lineage and impact analysis. It primarily focuses on serverless compute for processing S3 events rather than providing comprehensive data governance features.
Advanced Data Transformation Features: AWS Glue includes advanced data transformation features like automatic schema evolution, type inference, and inferred partitioning capabilities. These features simplify the process of schema evolution in data lakes and provide powerful options for optimizing data queries and performance. While S3-Lambda allows custom code execution on S3 events, it does not offer the same level of built-in advanced data transformation capabilities as Glue.
In summary, AWS Glue is a full-fledged ETL service with comprehensive features for data integration and transformation. S3-Lambda primarily serves as a serverless computing service for processing S3 events with limited data governance capabilities.
We need to perform ETL from several databases into a data warehouse or data lake. We want to
- keep raw and transformed data available to users to draft their own queries efficiently
- give users the ability to give custom permissions and SSO
- move between open-source on-premises development and cloud-based production environments
We want to use inexpensive Amazon EC2 instances only on medium-sized data set 16GB to 32GB feeding into Tableau Server or PowerBI for reporting and data analysis purposes.
You could also use AWS Lambda and use Cloudwatch event schedule if you know when the function should be triggered. The benefit is that you could use any language and use the respective database client.
But if you orchestrate ETLs then it makes sense to use Apache Airflow. This requires Python knowledge.
Though we have always built something custom, Apache airflow (https://airflow.apache.org/) stood out as a key contender/alternative when it comes to open sources. On the commercial offering, Amazon Redshift combined with Amazon Kinesis (for complex manipulations) is great for BI, though Redshift as such is expensive.
You may want to look into a Data Virtualization product called Conduit. It connects to disparate data sources in AWS, on prem, Azure, GCP, and exposes them as a single unified Spark SQL view to PowerBI (direct query) or Tableau. Allows auto query and caching policies to enhance query speeds and experience. Has a GPU query engine and optimized Spark for fallback. Can be deployed on your AWS VM or on prem, scales up and out. Sounds like the ideal solution to your needs.
I have to collect different data from multiple sources and store them in a single cloud location. Then perform cleaning and transforming using PySpark, and push the end results to other applications like reporting tools, etc. What would be the best solution? I can only think of Azure Data Factory + Databricks. Are there any alternatives to #AWS services + Databricks?
Hi all,
Currently, we need to ingest the data from Amazon S3 to DB either Amazon Athena or Amazon Redshift. But the problem with the data is, it is in .PSV (pipe separated values) format and the size is also above 200 GB. The query performance of the timeout in Athena/Redshift is not up to the mark, too slow while compared to Google BigQuery. How would I optimize the performance and query result time? Can anyone please help me out?
you can use aws glue service to convert you pipe format data to parquet format , and thus you can achieve data compression . Now you should choose Redshift to copy your data as it is very huge. To manage your data, you should partition your data in S3 bucket and also divide your data across the redshift cluster
First of all you should make your choice upon Redshift or Athena based on your use case since they are two very diferent services - Redshift is an enterprise-grade MPP Data Warehouse while Athena is a SQL layer on top of S3 with limited performance. If performance is a key factor, users are going to execute unpredictable queries and direct and managing costs are not a problem I'd definitely go for Redshift. If performance is not so critical and queries will be predictable somewhat I'd go for Athena.
Once you select the technology you'll need to optimize your data in order to get the queries executed as fast as possible. In both cases you may need to adapt the data model to fit your queries better. In the case you go for Athena you'd also proabably need to change your file format to Parquet or Avro and review your partition strategy depending on your most frequent type of query. If you choose Redshift you'll need to ingest the data from your files into it and maybe carry out some tuning tasks for performance gain.
I'll recommend Redshift for now since it can address a wider range of use cases, but we could give you better advice if you described your use case in depth.
It depend of the nature of your data (structured or not?) and of course your queries (ad-hoc or predictible?). For example you can look at partitioning and columnar format to maximize MPP capabilities for both Athena and Redshift
you can change your PSV fomat data to parquet file format with AWS GLUE and then your query performance will be improved
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