Need advice about which tool to choose?Ask the StackShare community!
AWS Glue vs Impala: What are the differences?
Introduction:
AWS Glue and Impala are both popular technologies used for data processing and analysis. While they share some similarities, there are key differences between the two that make each suitable for different use cases. This Markdown code will highlight and explain these differences in a clear and concise manner.
1. Data Processing Engine:
AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy to prepare and load data for analytics. It provides a serverless environment for running ETL jobs on various data sources, such as Amazon S3, Amazon RDS, and more. On the other hand, Impala is an open-source massively parallel processing SQL query engine built specifically for Apache Hadoop. It provides fast, interactive SQL queries on large datasets stored in Hadoop Distributed File System (HDFS).
2. Integration with Ecosystem:
AWS Glue seamlessly integrates with other AWS services, allowing users to easily combine data from various sources and perform analytics. It provides built-in integration with Amazon Redshift, Amazon Athena, and Amazon QuickSight, among others. In contrast, Impala is tightly integrated with the Hadoop ecosystem, utilizing the Hadoop stack for storage and Apache Hive for metadata management. It can leverage data stored in HDFS and can also query data in HBase and Apache Kudu.
3. Query Language:
AWS Glue supports ETL development using PySpark and Apache Spark. It enables users to write ETL scripts in Python or SparkSQL, providing flexibility and power for data transformation tasks. On the other hand, Impala uses SQL-based queries, similar to traditional relational database systems. It supports ANSI SQL and provides a familiar interface for users with SQL knowledge, making it easier to write and execute queries.
4. Performance and Scalability:
AWS Glue provides automatic scaling for processing large volumes of data. It can handle jobs of varying sizes and scale resources accordingly, ensuring efficient use of computing power. Impala, being a distributed query engine, also offers scalability by distributing workloads across a cluster of machines. It can process queries in parallel, enabling fast query response times and high concurrency.
5. Data Storage:
With AWS Glue, data can be stored in various formats, including CSV, JSON, Parquet, and more. It supports both structured and semi-structured data, providing flexibility for different data types. In contrast, Impala utilizes HDFS for data storage, which is optimized for handling large-scale data processing. It stores data in a distributed manner, spreading it across multiple nodes for increased fault tolerance and performance.
6. Cost and Pricing Model:
AWS Glue pricing is based on the number of Data Processing Units (DPUs) used during job execution, along with the amount of data processed and stored. It offers a pay-as-you-go model, allowing users to pay only for the resources utilized. Impala, being an open-source technology, is free to use. However, users need to consider the cost of managing and maintaining the infrastructure, which includes resources like storage, compute, and network.
In summary, AWS Glue and Impala are both powerful tools for data processing and analytics. AWS Glue provides a managed ETL service with seamless integration to other AWS services, supporting different data sources and using PySpark or SparkSQL for ETL development. Impala, on the other hand, is an open-source SQL query engine focused on Hadoop ecosystem, providing fast query performance on large datasets stored in HDFS. Choose AWS Glue for serverless ETL capabilities and integration with AWS services, or Impala for high-performance SQL queries on Hadoop.
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 AWS Glue
- Managed Hive Metastore9
Pros of Apache Impala
- Super fast11
- Massively Parallel Processing1
- Load Balancing1
- Replication1
- Scalability1
- Distributed1
- High Performance1
- Open Sourse1
Sign up to add or upvote prosMake informed product decisions
What is AWS Glue?
What is Apache Impala?
Need advice about which tool to choose?Ask the StackShare community!
What companies use AWS Glue?
What companies use Apache Impala?
Sign up to get full access to all the companiesMake informed product decisions
What tools integrate with AWS Glue?
What tools integrate with Apache Impala?
Sign up to get full access to all the tool integrationsMake informed product decisions
Blog Posts
+16