Overview

Apache Kudu

Stacks71

Followers259

Votes10

GitHub Stars828

Forks282

AWS Glue

Stacks461

Followers819

Votes9

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.

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Advice on Apache Kudu, AWS Glue

Vamshi

Data Engineer at Tata Consultancy Services

May 29, 2020

Needs adviceon

PySpark

Azure Data Factory

Databricks

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?

269k views269k

Comments

datocrats-org

Jul 29, 2020

Needs adviceon

Amazon EC2

Tableau

PowerBI

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.

319k views319k

Comments

Pavithra

Mar 12, 2020

Needs adviceon

Amazon S3

Amazon Athena

Amazon Redshift

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?

522k views522k

Comments

Detailed Comparison

Apache Kudu	AWS Glue
A new addition to the open source Apache Hadoop ecosystem, Kudu completes Hadoop's storage layer to enable fast analytics on fast data.	A fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics.
-	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.
Statistics
GitHub Stars 828	GitHub Stars -
GitHub Forks 282	GitHub Forks -
Stacks 71	Stacks 461
Followers 259	Followers 819
Votes 10	Votes 9
Pros & Cons
Pros 10 Realtime Analytics Cons 1 Restart time	Pros 9 Managed Hive Metastore
Integrations
Hadoop	Amazon Redshift Amazon S3 Amazon RDS Amazon Athena MySQL Microsoft SQL Server Amazon EMR Amazon Aurora Oracle Amazon RDS for PostgreSQL

What are some alternatives to Apache Kudu, AWS Glue?

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.

Presto

Distributed SQL Query Engine for Big Data

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.

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.