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  1. Stackups
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  4. Big Data Tools
  5. Apache Spark vs Vespa

Apache Spark vs Vespa

OverviewDecisionsComparisonAlternatives

Overview

Apache Spark
Apache Spark
Stacks3.1K
Followers3.5K
Votes140
GitHub Stars42.2K
Forks28.9K
Vespa
Vespa
Stacks12
Followers29
Votes0
GitHub Stars6.5K
Forks675

Apache Spark vs Vespa: What are the differences?

<Apache Spark vs. Vespa>

1. **Architecture**: Apache Spark is primarily a distributed data processing framework, focusing on data processing, analytics, and machine learning tasks. Vespa, on the other hand, is a scalable, real-time big data serving platform optimized for many small lookups and updates, making it suitable for serving applications such as recommendation systems and search engines.
2. **Scalability**: Apache Spark is designed for processing and analyzing large datasets in a distributed manner, making it suitable for big data applications. Vespa is optimized for handling many small, latency-sensitive queries and updates, making it more suitable for real-time serving applications.
3. **Use Cases**: Apache Spark is commonly used for tasks such as data processing, analytics, and machine learning, where large-scale data processing is required. Vespa is more suited for applications that require real-time, low-latency data serving, such as personalized recommendations or search engines.
4. **Programming Models**: Apache Spark provides a variety of APIs and libraries for building data processing and analytics workflows, including SQL, streaming, and machine learning libraries. Vespa offers a specialized query language and APIs tailored for building data serving applications that require real-time responses and low latencies.
5. **Community Support**: Apache Spark has a large and active community of developers contributing to the project, providing a wide range of resources, tutorials, and support. Vespa, while also open source, has a smaller community focused on real-time serving applications.
6. **Deployment**: Apache Spark can be deployed on a variety of cluster managers and cloud platforms, giving users flexibility in how they deploy and manage their Spark applications. Vespa, on the other hand, is designed to be deployed as a self-contained system, optimized for running on a cluster of nodes to provide high availability and low latency for serving applications.

In Summary, Apache Spark and Vespa differ in their architecture, scalability, use cases, programming models, community support, and deployment options, catering to different needs in the big data and real-time serving domains.

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Advice on Apache Spark, Vespa

Nilesh
Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceonElasticsearchElasticsearchKafkaKafka

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.

576k views576k
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Detailed Comparison

Apache Spark
Apache Spark
Vespa
Vespa

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.

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.

Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk;Write applications quickly in Java, Scala or Python;Combine SQL, streaming, and complex analytics;Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, S3
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Statistics
GitHub Stars
42.2K
GitHub Stars
6.5K
GitHub Forks
28.9K
GitHub Forks
675
Stacks
3.1K
Stacks
12
Followers
3.5K
Followers
29
Votes
140
Votes
0
Pros & Cons
Pros
  • 61
    Open-source
  • 48
    Fast and Flexible
  • 8
    One platform for every big data problem
  • 8
    Great for distributed SQL like applications
  • 6
    Easy to install and to use
Cons
  • 4
    Speed
No community feedback yet
Integrations
No integrations available
Hadoop
Hadoop
Pig
Pig

What are some alternatives to Apache Spark, Vespa?

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.

Azure Synapse

Azure Synapse

It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

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