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  1. Stackups
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  3. Databases
  4. Big Data Tools
  5. Apache Spark vs BDS

Apache Spark vs BDS

OverviewDecisionsComparisonAlternatives

Overview

Apache Spark
Apache Spark
Stacks3.1K
Followers3.5K
Votes140
GitHub Stars42.2K
Forks28.9K
BDS
BDS
Stacks3
Followers9
Votes0
GitHub Stars996
Forks61

Apache Spark vs BDS: What are the differences?

Developers describe Apache Spark as "Fast and general engine for large-scale data processing". 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. On the other hand, BDS is detailed as "*Blockchain data parsing and persisting results *". It is a realtime data aggregating, analyzing and visualization service for chain-like unstructured data from all kinds of 3rd party Blockchains.

Apache Spark belongs to "Big Data Tools" category of the tech stack, while BDS can be primarily classified under "Blockchain".

Some of the features offered by Apache Spark are:

  • 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

On the other hand, BDS provides the following key features:

  • Cover dozens of well-known Blockchain projects, including BTC, ETH, LTC, XRP, BCH, etc
  • Provide an interactive data visualization BI tool
  • Support standard SQL Query statements so that complex query logics can be implemented easily

Apache Spark and BDS are both open source tools. Apache Spark with 23.2K GitHub stars and 19.9K forks on GitHub appears to be more popular than BDS with 324 GitHub stars and 42 GitHub forks.

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Advice on Apache Spark, BDS

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
Comments

Detailed Comparison

Apache Spark
Apache Spark
BDS
BDS

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 is a realtime data aggregating, analyzing and visualization service for chain-like unstructured data from all kinds of 3rd party Blockchains.

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
Cover dozens of well-known Blockchain projects, including BTC, ETH, LTC, XRP, BCH, etc;Provide an interactive data visualization BI tool;Support standard SQL Query statements so that complex query logics can be implemented easily;Provide products that query data on the Blockchain in China and it also provides data visualization BI tools
Statistics
GitHub Stars
42.2K
GitHub Stars
996
GitHub Forks
28.9K
GitHub Forks
61
Stacks
3.1K
Stacks
3
Followers
3.5K
Followers
9
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
Grafana
Grafana
PostgreSQL
PostgreSQL
Kafka
Kafka
Microsoft SQL Server
Microsoft SQL Server
Confluent
Confluent

What are some alternatives to Apache Spark, BDS?

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.

Ethereum

Ethereum

A decentralized platform for applications that run exactly as programmed without any chance of fraud, censorship or third-party interference.

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