Apache Spark vs Vertica

Overview

Vertica

Stacks88

Followers120

Votes16

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Apache Spark vs Vertica: What are the differences?

Introduction: Apache Spark and Vertica are both powerful tools used in big data processing and analytics. However, they have distinct differences that make them suitable for different use cases.

Scalability: Apache Spark is designed for horizontal scalability, meaning it can easily scale out by adding more nodes to handle larger datasets. On the other hand, Vertica is a columnar database that is optimized for scaling up by adding more resources to a single machine. This makes Vertica better suited for scenarios where vertical scalability is preferred over horizontal scalability.
Data Processing Paradigm: Apache Spark uses an in-memory processing engine that allows for real-time data processing and analytics. In contrast, Vertica relies on disk-based storage for data processing and analytics, which can lead to slower performance compared to Spark, especially for real-time processing workloads.
Ease of Use: Apache Spark provides a more user-friendly interface and programming model compared to Vertica, making it easier for developers to write and manage complex data processing tasks. Additionally, Spark supports multiple programming languages like Java, Scala, and Python, while Vertica primarily uses SQL for data querying and manipulation.
Data Storage: While both Apache Spark and Vertica can handle large volumes of data, they store data in different ways. Spark stores data in Resilient Distributed Datasets (RDDs) and DataFrames, while Vertica stores data in a columnar format. This difference in storage format can impact the performance of certain types of data processing tasks.
Cost: When it comes to cost, Vertica is generally more expensive compared to Apache Spark. Vertica requires licensing fees for enterprise use, while Spark is open source and can be used without any additional costs. This can be a significant factor to consider for organizations with budget constraints.
Use Cases: Apache Spark is well-suited for real-time data processing, machine learning, and streaming analytics applications. Vertica, on the other hand, is commonly used for data warehousing, business intelligence, and ad-hoc querying scenarios. Understanding the specific use cases of each tool can help organizations choose the right solution for their data processing needs.

In Summary, Apache Spark and Vertica differ in scalability, data processing paradigm, ease of use, data storage, cost, and use cases. Organizations should consider these differences when selecting a tool for their big data processing and analytics needs.

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Advice on Vertica, Apache Spark

Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceon

Elasticsearch

Kafka

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.

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Comments

Detailed Comparison

Vertica	Apache Spark
It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.	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.
Analyze All of Your Data. No longer move data or settle for siloed views;Achieve Scale and Performance;Fear of growing data volumes and users is a thing of the past;Future-Proof Your Analytics	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
Statistics
GitHub Stars -	GitHub Stars 42.2K
GitHub Forks -	GitHub Forks 28.9K
Stacks 88	Stacks 3.1K
Followers 120	Followers 3.5K
Votes 16	Votes 140
Pros & Cons
Pros 3 Shared nothing or shared everything architecture 1 Vertica is the only product which offers partition prun 1 Query-Optimized Storage 1 Fully automated Database Designer tool 1 Near-Real-Time Analytics in pure Column Store	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
Integrations
Oracle Golang MongoDB MySQL Sass Mode PowerBI Tableau Talend	No integrations available

What are some alternatives to Vertica, Apache Spark?

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

Related Comparisons

Apache Spark vs Vertica: What are the differences?

Introduction: Apache Spark and Vertica are both powerful tools used in big data processing and analytics. However, they have distinct differences that make them suitable for different use cases.

Scalability: Apache Spark is designed for horizontal scalability, meaning it can easily scale out by adding more nodes to handle larger datasets. On the other hand, Vertica is a columnar database that is optimized for scaling up by adding more resources to a single machine. This makes Vertica better suited for scenarios where vertical scalability is preferred over horizontal scalability.
Data Processing Paradigm: Apache Spark uses an in-memory processing engine that allows for real-time data processing and analytics. In contrast, Vertica relies on disk-based storage for data processing and analytics, which can lead to slower performance compared to Spark, especially for real-time processing workloads.
Ease of Use: Apache Spark provides a more user-friendly interface and programming model compared to Vertica, making it easier for developers to write and manage complex data processing tasks. Additionally, Spark supports multiple programming languages like Java, Scala, and Python, while Vertica primarily uses SQL for data querying and manipulation.
Data Storage: While both Apache Spark and Vertica can handle large volumes of data, they store data in different ways. Spark stores data in Resilient Distributed Datasets (RDDs) and DataFrames, while Vertica stores data in a columnar format. This difference in storage format can impact the performance of certain types of data processing tasks.
Cost: When it comes to cost, Vertica is generally more expensive compared to Apache Spark. Vertica requires licensing fees for enterprise use, while Spark is open source and can be used without any additional costs. This can be a significant factor to consider for organizations with budget constraints.
Use Cases: Apache Spark is well-suited for real-time data processing, machine learning, and streaming analytics applications. Vertica, on the other hand, is commonly used for data warehousing, business intelligence, and ad-hoc querying scenarios. Understanding the specific use cases of each tool can help organizations choose the right solution for their data processing needs.

Apache Spark vs Vertica

Overview