Apache Spark vs BlazingSQL

Overview

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

BlazingSQL

Stacks1

Followers23

Votes0

Apache Spark vs BlazingSQL: What are the differences?

Introduction:

Apache Spark and BlazingSQL are both powerful tools for data processing and analysis, but they have key differences that set them apart. Below are the main disparities between the two technologies.

1. Performance: Apache Spark is known for its in-memory processing capabilities, making it faster than traditional disk-based systems like Hadoop. On the other hand, BlazingSQL leverages GPU acceleration, enabling it to handle large datasets with significant speed improvements compared to CPU-based processing.

2. Data Source Compatibility: Apache Spark supports a wide range of data sources through its connectors, enabling users to work with various file formats and databases seamlessly. In contrast, BlazingSQL is primarily designed for working with GPU-accelerated data sources, making it a convenient choice for GPU-oriented workflows.

3. Ease of Use: Apache Spark's complex API and learning curve can be challenging for beginners, requiring a solid understanding of distributed computing concepts. In comparison, BlazingSQL offers a more SQL-focused interface, making it easier for SQL users to transition to GPU-accelerated computing without extensive retraining.

4. Scalability: Spark is well-known for its scalability, allowing users to process massive datasets across clusters of machines efficiently. While BlazingSQL can also scale effectively with the use of GPUs, it may have limitations in scalability when compared to Spark in certain distributed computing scenarios.

5. Community Support: Apache Spark has a robust community with a wealth of resources, forums, and libraries available for users to leverage. In contrast, BlazingSQL's community is relatively newer and may have a smaller user base, which can impact the availability of support and resources for users encountering issues.

6. Ecosystem Integration: While both Apache Spark and BlazingSQL can integrate with various data processing and visualization tools, Spark's mature ecosystem and integration capabilities with other big data technologies like Hadoop and Kafka give it an edge in the broader data ecosystem. On the other hand, BlazingSQL's integration options may be more limited in comparison.

In Summary, Apache Spark and BlazingSQL differ in terms of performance, data source compatibility, ease of use, scalability, community support, and ecosystem integration, offering users distinct advantages and considerations based on their specific data processing needs.

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Advice on Apache Spark, BlazingSQL

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	BlazingSQL
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.	It's a GPU accelerated SQL engine built on top of the RAPIDS ecosystem. RAPIDS is based on the Apache Arrow columnar memory format, and cuDF is a GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating data.
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 42.2K	GitHub Stars -
GitHub Forks 28.9K	GitHub Forks -
Stacks 3.1K	Stacks 1
Followers 3.5K	Followers 23
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	Amazon S3 Python Hadoop

What are some alternatives to Apache Spark, BlazingSQL?

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 BlazingSQL: What are the differences?

Introduction:

Apache Spark and BlazingSQL are both powerful tools for data processing and analysis, but they have key differences that set them apart. Below are the main disparities between the two technologies.

Apache Spark vs BlazingSQL

Overview