269k views269k
CommentsAWS Glue vs Dremio
Overview
Share your Stack
Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.
CLI (Node.js)
Manual
Advice on AWS Glue, Dremio
Detailed Comparison
A fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics. | Dremio—the data lake engine, operationalizes your data lake storage and speeds your analytics processes with a high-performance and high-efficiency query engine while also democratizing data access for data scientists and analysts. |
Easy - AWS Glue automates much of the effort in building, maintaining, and running ETL jobs. AWS Glue crawls your data sources, identifies data formats, and suggests schemas and transformations. AWS Glue automatically generates the code to execute your data transformations and loading processes.; Integrated - AWS Glue is integrated across a wide range of AWS services.; Serverless - AWS Glue is serverless. There is no infrastructure to provision or manage. AWS Glue handles provisioning, configuration, and scaling of the resources required to run your ETL jobs on a fully managed, scale-out Apache Spark environment. You pay only for the resources used while your jobs are running.; Developer Friendly - AWS Glue generates ETL code that is customizable, reusable, and portable, using familiar technology - Scala, Python, and Apache Spark. You can also import custom readers, writers and transformations into your Glue ETL code. Since the code AWS Glue generates is based on open frameworks, there is no lock-in. You can use it anywhere. | Democratize all your data;
Make your data engineers more productive;
Accelerate your favorite tools;
Self service, for everybody
|
Statistics | |
Stacks 461 | Stacks 116 |
Followers 819 | Followers 348 |
Votes 9 | Votes 8 |
Pros & Cons | |
Pros
| Pros
Cons
|
Integrations | |
What are some alternatives to AWS Glue, Dremio?
Google BigQuery
Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.
Apache Spark
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
Amazon Redshift
It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.
Qubole
Qubole is a cloud based service that makes big data easy for analysts and data engineers.
Presto
Distributed SQL Query Engine for Big Data
Amazon EMR
It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.
Amazon Athena
Amazon 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 that you run.
Apache Flink
Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.
lakeFS
It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.
Druid
Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.
