Apache Spark vs Delta Lake

Overview

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Delta Lake

Stacks105

Followers315

Votes0

GitHub Stars8.4K

Forks1.9K

Apache Spark vs Delta Lake: What are the differences?

Introduction

Apache Spark and Delta Lake are two popular big data technologies used for processing and analyzing large datasets. While both technologies are often used together, they have some key differences that set them apart.

Data Storage and Processing: The key difference between Apache Spark and Delta Lake lies in their approach to data storage and processing. Apache Spark is primarily a distributed computing system that provides an interface for processing data in parallel. It supports various data formats and can process both batch and streaming data. On the other hand, Delta Lake is an open-source storage layer that operates on top of existing data lakes and provides ACID (Atomicity, Consistency, Isolation, Durability) transactions. It allows data engineers and data scientists to handle large-scale datasets with reliability and consistency.
Data Consistency and Reliability: Delta Lake provides atomic writes and reads, as well as schema enforcement and schema evolution capabilities. This ensures that data is written and read in an all-or-nothing manner, making it easier to maintain data consistency and reliability. Apache Spark, on the other hand, does not provide built-in support for data consistency and reliability. Data engineers and developers need to implement custom logic to ensure data consistency and reliability.
Data Versioning and Time Travel: Delta Lake is designed to provide built-in versioning and time travel capabilities. It allows users to query and access older versions of data, making it easier to track changes over time. In contrast, Apache Spark does not provide built-in support for data versioning and time travel. Data engineers need to implement custom logic or use additional tools to achieve similar functionality.
Data Quality Management: Delta Lake includes features like schema validation and data quality checks, which help ensure that data remains consistent and of high quality. It provides mechanisms to enforce schema evolution rules and perform data validation during write operations. Apache Spark does not have built-in support for data quality management. Data engineers need to implement custom logic to validate and enforce data quality rules.
Optimized Performance: Delta Lake optimizes data reads and writes by using various techniques like Z-ordering, data skipping, and caching. These optimizations improve query performance and reduce the amount of data scanned during read operations. Apache Spark also provides optimizations for data processing, but it does not have the same level of built-in optimization capabilities as Delta Lake.
Ecosystem Integration: Apache Spark has a vibrant ecosystem and supports integration with various data processing and analytics tools. It provides connectors for different data sources and supports integration with popular frameworks like Apache Hadoop, Apache Kafka, and Apache Hive. Delta Lake, being a storage layer built on top of data lakes, can seamlessly integrate with Apache Spark and leverage its ecosystem.

In summary, Apache Spark is a distributed computing system primarily focused on data processing, while Delta Lake is a storage layer that provides reliability, consistency, and versioning capabilities on top of existing data lakes. Delta Lake's built-in support for data consistency, reliability, versioning, and data quality management sets it apart from Apache Spark.

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Advice on Apache Spark, Delta Lake

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	Delta Lake
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.	An open-source storage layer that brings ACID transactions to Apache Spark™ and big data workloads.
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	ACID Transactions; Scalable Metadata Handling; Time Travel (data versioning); Open Format; Unified Batch and Streaming Source and Sink; Schema Enforcement; Schema Evolution; 100% Compatible with Apache Spark API
Statistics
GitHub Stars 42.2K	GitHub Stars 8.4K
GitHub Forks 28.9K	GitHub Forks 1.9K
Stacks 3.1K	Stacks 105
Followers 3.5K	Followers 315
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 Amazon S3

What are some alternatives to Apache Spark, Delta Lake?

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.

Vertica

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

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.

Related Comparisons

Apache Spark vs Delta Lake: What are the differences?

Introduction

Data Storage and Processing: The key difference between Apache Spark and Delta Lake lies in their approach to data storage and processing. Apache Spark is primarily a distributed computing system that provides an interface for processing data in parallel. It supports various data formats and can process both batch and streaming data. On the other hand, Delta Lake is an open-source storage layer that operates on top of existing data lakes and provides ACID (Atomicity, Consistency, Isolation, Durability) transactions. It allows data engineers and data scientists to handle large-scale datasets with reliability and consistency.
Data Consistency and Reliability: Delta Lake provides atomic writes and reads, as well as schema enforcement and schema evolution capabilities. This ensures that data is written and read in an all-or-nothing manner, making it easier to maintain data consistency and reliability. Apache Spark, on the other hand, does not provide built-in support for data consistency and reliability. Data engineers and developers need to implement custom logic to ensure data consistency and reliability.
Data Versioning and Time Travel: Delta Lake is designed to provide built-in versioning and time travel capabilities. It allows users to query and access older versions of data, making it easier to track changes over time. In contrast, Apache Spark does not provide built-in support for data versioning and time travel. Data engineers need to implement custom logic or use additional tools to achieve similar functionality.
Data Quality Management: Delta Lake includes features like schema validation and data quality checks, which help ensure that data remains consistent and of high quality. It provides mechanisms to enforce schema evolution rules and perform data validation during write operations. Apache Spark does not have built-in support for data quality management. Data engineers need to implement custom logic to validate and enforce data quality rules.
Optimized Performance: Delta Lake optimizes data reads and writes by using various techniques like Z-ordering, data skipping, and caching. These optimizations improve query performance and reduce the amount of data scanned during read operations. Apache Spark also provides optimizations for data processing, but it does not have the same level of built-in optimization capabilities as Delta Lake.
Ecosystem Integration: Apache Spark has a vibrant ecosystem and supports integration with various data processing and analytics tools. It provides connectors for different data sources and supports integration with popular frameworks like Apache Hadoop, Apache Kafka, and Apache Hive. Delta Lake, being a storage layer built on top of data lakes, can seamlessly integrate with Apache Spark and leverage its ecosystem.

Apache Spark vs Delta Lake

Overview

Apache Spark vs Delta Lake: What are the differences?