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  1. Stackups
  2. Application & Data
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  4. Big Data Tools
  5. AWS Glue vs Vespa

AWS Glue vs Vespa

OverviewDecisionsComparisonAlternatives

Overview

Vespa
Vespa
Stacks12
Followers29
Votes0
GitHub Stars6.5K
Forks675
AWS Glue
AWS Glue
Stacks461
Followers819
Votes9

AWS Glue vs Vespa: What are the differences?

AWS Glue and Vespa are both powerful tools used in web development, but they have some key differences that set them apart from each other. In this article, we will explore and examine these differences in detail.
  1. Data Storage and Processing: AWS Glue is primarily used for data extraction, transformation, and loading (ETL) in the cloud. It provides a fully managed extract, transform, load (ETL) service that makes it easy to prepare and load data for analysis. On the other hand, Vespa is an open-source big data processing and analytics engine that is designed for high-performance, real-time applications. It combines fast indexing, efficient search, and powerful analytics in a single platform.

  2. Deployment: When it comes to deployment, there is a difference between AWS Glue and Vespa. AWS Glue is a fully managed service provided by Amazon Web Services, which means that users don't have to worry about managing the infrastructure. Vespa, on the other hand, requires users to set up and manage their own infrastructure. This gives users more control and flexibility over the deployment process.

  3. Ease of Use: Another key difference between AWS Glue and Vespa is their ease of use. AWS Glue provides a visual interface and a range of pre-built ETL transformations, making it user-friendly and highly accessible to non-technical users. Vespa, on the other hand, is more developer-oriented and requires some technical expertise to configure and set up.

  4. Scalability: Both AWS Glue and Vespa are designed to be highly scalable, but there is a difference in how they achieve scalability. AWS Glue automatically scales resources based on the volume of data being processed, ensuring that users have sufficient resources to handle their workload. Vespa, on the other hand, allows users to manually scale their infrastructure based on their specific needs.

  5. Supported Languages: AWS Glue primarily supports Python and Scala for writing ETL scripts, as well as Apache Spark for data processing. Vespa, on the other hand, supports a wide range of programming languages including Java, C++, and Python, giving users more flexibility in their development.

  6. Use Cases: Finally, AWS Glue and Vespa are used in different use cases. AWS Glue is commonly used for data integration, data warehousing, and big data processing, while Vespa is suitable for building real-time applications such as search engines, recommendation systems, and content personalization.

In summary, AWS Glue is a fully managed ETL service that excels at data extraction, transformation, and loading, while Vespa is an open-source big data processing and analytics engine designed for high-performance, real-time applications. Both tools have their strengths and are tailored to different use cases, so the choice between them depends on specific project requirements and user preferences.

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Advice on Vespa, AWS Glue

Aditya
Aditya

Mar 13, 2021

Review

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

220k views220k
Comments
Vamshi
Vamshi

Data Engineer at Tata Consultancy Services

May 29, 2020

Needs adviceonPySparkPySparkAzure Data FactoryAzure Data FactoryDatabricksDatabricks

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
datocrats-org

Jul 29, 2020

Needs adviceonAmazon EC2Amazon EC2TableauTableauPowerBIPowerBI

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

Detailed Comparison

Vespa
Vespa
AWS Glue
AWS Glue

Vespa is an engine for low-latency computation over large data sets. It stores and indexes your data such that queries, selection and processing over the data can be performed at serving time.

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
6.5K
GitHub Stars
-
GitHub Forks
675
GitHub Forks
-
Stacks
12
Stacks
461
Followers
29
Followers
819
Votes
0
Votes
9
Pros & Cons
No community feedback yet
Pros
  • 9
    Managed Hive Metastore
Integrations
Hadoop
Hadoop
Pig
Pig
Amazon Redshift
Amazon Redshift
Amazon S3
Amazon S3
Amazon RDS
Amazon RDS
Amazon Athena
Amazon Athena
MySQL
MySQL
Microsoft SQL Server
Microsoft SQL Server
Amazon EMR
Amazon EMR
Amazon Aurora
Amazon Aurora
Oracle
Oracle
Amazon RDS for PostgreSQL
Amazon RDS for PostgreSQL

What are some alternatives to Vespa, AWS Glue?

Apache Spark

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

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

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

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

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

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

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

Splunk

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

Apache Impala

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.

Vertica

Vertica

It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.

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