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AWS Glue vs Apache Kudu: What are the differences?
ACID Compliance: AWS Glue is a fully managed extract, transform, and load (ETL) service that allows businesses to prepare and transform their data for analytics. It supports ACID (Atomicity, Consistency, Isolation, Durability) transactions, ensuring data integrity and consistency during the ETL process. On the other hand, Apache Kudu is a storage system that provides fast analytics on fast data. While Kudu also supports ACID transactions, it focuses more on real-time ingestion and interactive analytic workloads.
Data Format Support: AWS Glue supports various data formats such as Avro, Parquet, JSON, CSV, and more. It provides easy integration with other AWS services like Amazon S3, Amazon Redshift, and Amazon RDS. Apache Kudu, on the other hand, uses columnar storage to provide efficient compression and encoding of data. It also supports data storage in popular formats like Parquet, Avro, and RCFile.
Data Ingestion: AWS Glue supports data ingestion from various sources including JDBC, Amazon S3, Amazon DynamoDB, Salesforce, and more. It provides connectors and crawlers to automatically discover and catalog data, making it easier to work with different datasets. Apache Kudu supports data ingestion through Apache Kafka, Hadoop, or directly through its APIs using various programming languages like Java, C++, Python, and more.
Data Querying: AWS Glue allows you to run SQL queries on structured and semi-structured data using its Apache Spark-based engine. It also supports data exploration and analytics through its integration with AWS Athena and Amazon Redshift Spectrum. Apache Kudu, on the other hand, provides a columnar storage layout that allows efficient querying and aggregation of data. It supports traditional SQL and analytical frameworks like Apache Impala for interactive queries.
Data Updates: AWS Glue allows for both batch and streaming data processing, making it suitable for handling real-time data. It provides capabilities to handle data updates, insertions, and deletions using its transformation jobs. Apache Kudu, on the other hand, focuses more on fast data ingestion and updates. It provides near-real-time updates and is designed for use cases that require frequent data updates and quick query response times.
Scalability and Deployment: AWS Glue is a fully managed service provided by AWS, which means it automatically scales based on the workload and is easy to deploy. It also integrates well with other AWS services and provides additional capabilities like data cataloging and schema evolution. Apache Kudu, on the other hand, can be deployed on-premise or in the cloud and offers high scalability and fault tolerance. It can handle large data volumes efficiently and provides replication and automated data placement to ensure data reliability.
In Summary, AWS Glue is a fully managed ETL service that focuses on preparing and transforming data for analytics, while Apache Kudu is a storage system that provides fast analytics on fast data, with a focus on real-time ingestion and interactive analytic workloads.
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
Pros of Apache Kudu
- Realtime Analytics10
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- Managed Hive Metastore9
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Cons of Apache Kudu
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