Apache Spark vs dbt

Overview

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

dbt

Stacks518

Followers461

Votes16

Apache Spark vs dbt: What are the differences?

Introduction:

Apache Spark and dbt are both popular tools used in data processing and analysis. While they have some similarities, there are several key differences between the two. In this article, we will explore these differences in detail.

Architecture: One of the key differences between Apache Spark and dbt lies in their architecture. Apache Spark is a distributed computing system that allows for the parallel processing of large datasets across a cluster of computers. On the other hand, dbt is an SQL-based transformation tool that operates on a single machine. This fundamental difference in architecture allows Apache Spark to handle big data workloads more efficiently, while dbt is better suited for smaller datasets.
Processing Engine: Apache Spark and dbt use different processing engines. Apache Spark leverages an in-memory computing engine, which enables it to perform real-time data processing at a much faster speed. dbt, on the other hand, uses a traditional disk-based processing engine, which is slower in comparison. This difference in processing engines gives Apache Spark an advantage when it comes to handling complex data processing tasks.
Data Source Support: Another important difference between Apache Spark and dbt is the range of data sources they support. Apache Spark has extensive support for various data sources, including Hadoop Distributed File System (HDFS), Amazon S3, and more. This makes it easier to integrate Apache Spark with different data platforms and extract data from diverse sources. dbt, on the other hand, has limited data source support and primarily focuses on SQL-based databases.
Transformation Capabilities: When it comes to data transformations, Apache Spark offers a wide range of built-in operators and functions that facilitate complex data transformations. It provides a flexible and powerful programming interface that allows users to manipulate data using SQL, Python, Scala, or R. dbt, on the other hand, is primarily focused on SQL-based transformations and lacks the versatility offered by Apache Spark.
Data Modeling: Apache Spark and dbt approach data modeling differently. Apache Spark provides a GraphX library that enables graph-parallel computation, making it easier to model and analyze graph databases. It also supports machine learning and graph algorithms out of the box. dbt, on the other hand, does not have built-in support for graph modeling or machine learning and is primarily designed for SQL-based data modeling.
Data Governance and Collaboration: Apache Spark and dbt have different capabilities when it comes to data governance and collaboration. Apache Spark provides features like access control, auditing, and data lineage, which are crucial for ensuring data governance and compliance. It also supports collaborative development by providing integration with version control systems like Git. On the other hand, dbt does not have built-in support for data governance or collaborative development.

In summary, Apache Spark is a distributed computing system with advanced processing capabilities, extensive data source support, and versatile transformation capabilities. On the other hand, dbt is a SQL-based transformation tool that operates on a single machine and is primarily focused on SQL-based data modeling.

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Advice on Apache Spark, dbt

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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Detailed Comparison

Apache Spark	dbt
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.	dbt is a transformation workflow that lets teams deploy analytics code following software engineering best practices like modularity, portability, CI/CD, and documentation. Now anyone who knows SQL can build production-grade data pipelines.
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	Code compiler; Package management; Seed file loader; Data snapshots; Understand raw data sources; Tests; Documentation; CI/CD
Statistics
GitHub Stars 42.2K	GitHub Stars -
GitHub Forks 28.9K	GitHub Forks -
Stacks 3.1K	Stacks 518
Followers 3.5K	Followers 461
Votes 140	Votes 16
Pros & Cons
Pros 61 Open-source 48 Fast and Flexible 8 Great for distributed SQL like applications 8 One platform for every big data problem 6 Easy to install and to use Cons 4 Speed	Pros 5 Easy for SQL programmers to learn 3 Reusable Macro 2 Schedule Jobs 2 CI/CD 2 Modularity, portability, CI/CD, and documentation Cons 1 People will have have only sql skill set at the end 1 Very bad for people from learning perspective 1 Only limited to SQL 1 Cant do complex iterations , list comprehensions etc .
Integrations
No integrations available	Exasol Snowflake Materialize Presto Amazon Redshift Google BigQuery PostgreSQL Dremio Databricks Azure Synapse

What are some alternatives to Apache Spark, dbt?

dbForge Studio for MySQL

It is the universal MySQL and MariaDB client for database management, administration and development. With the help of this intelligent MySQL client the work with data and code has become easier and more convenient. This tool provides utilities to compare, synchronize, and backup MySQL databases with scheduling, and gives possibility to analyze and report MySQL tables data.

dbForge Studio for Oracle

It is a powerful integrated development environment (IDE) which helps Oracle SQL developers to increase PL/SQL coding speed, provides versatile data editing tools for managing in-database and external data.

dbForge Studio for PostgreSQL

It is a GUI tool for database development and management. The IDE for PostgreSQL allows users to create, develop, and execute queries, edit and adjust the code to their requirements in a convenient and user-friendly interface.

dbForge Studio for SQL Server

It is a powerful IDE for SQL Server management, administration, development, data reporting and analysis. The tool will help SQL developers to manage databases, version-control database changes in popular source control systems, speed up routine tasks, as well, as to make complex database changes.

Liquibase

Liquibase is th leading open-source tool for database schema change management. Liquibase helps teams track, version, and deploy database schema and logic changes so they can automate their database code process with their app code process.

Sequel Pro

Sequel Pro is a fast, easy-to-use Mac database management application for working with MySQL databases.

DBeaver

It is a free multi-platform database tool for developers, SQL programmers, database administrators and analysts. Supports all popular databases: MySQL, PostgreSQL, SQLite, Oracle, DB2, SQL Server, Sybase, Teradata, MongoDB, Cassandra, Redis, etc.

Presto

Distributed SQL Query Engine for Big Data

dbForge SQL Complete

It is an IntelliSense add-in for SQL Server Management Studio, designed to provide the fastest T-SQL query typing ever possible.

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.

Related Comparisons

Apache Spark vs dbt: What are the differences?

Introduction:

Architecture: One of the key differences between Apache Spark and dbt lies in their architecture. Apache Spark is a distributed computing system that allows for the parallel processing of large datasets across a cluster of computers. On the other hand, dbt is an SQL-based transformation tool that operates on a single machine. This fundamental difference in architecture allows Apache Spark to handle big data workloads more efficiently, while dbt is better suited for smaller datasets.
Processing Engine: Apache Spark and dbt use different processing engines. Apache Spark leverages an in-memory computing engine, which enables it to perform real-time data processing at a much faster speed. dbt, on the other hand, uses a traditional disk-based processing engine, which is slower in comparison. This difference in processing engines gives Apache Spark an advantage when it comes to handling complex data processing tasks.
Data Source Support: Another important difference between Apache Spark and dbt is the range of data sources they support. Apache Spark has extensive support for various data sources, including Hadoop Distributed File System (HDFS), Amazon S3, and more. This makes it easier to integrate Apache Spark with different data platforms and extract data from diverse sources. dbt, on the other hand, has limited data source support and primarily focuses on SQL-based databases.
Transformation Capabilities: When it comes to data transformations, Apache Spark offers a wide range of built-in operators and functions that facilitate complex data transformations. It provides a flexible and powerful programming interface that allows users to manipulate data using SQL, Python, Scala, or R. dbt, on the other hand, is primarily focused on SQL-based transformations and lacks the versatility offered by Apache Spark.
Data Modeling: Apache Spark and dbt approach data modeling differently. Apache Spark provides a GraphX library that enables graph-parallel computation, making it easier to model and analyze graph databases. It also supports machine learning and graph algorithms out of the box. dbt, on the other hand, does not have built-in support for graph modeling or machine learning and is primarily designed for SQL-based data modeling.
Data Governance and Collaboration: Apache Spark and dbt have different capabilities when it comes to data governance and collaboration. Apache Spark provides features like access control, auditing, and data lineage, which are crucial for ensuring data governance and compliance. It also supports collaborative development by providing integration with version control systems like Git. On the other hand, dbt does not have built-in support for data governance or collaborative development.

Apache Spark vs dbt

Overview