Apache Spark vs Qubole

Overview

Qubole

Stacks36

Followers104

Votes67

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Apache Spark vs Qubole: What are the differences?

## Introduction
Apache Spark and Qubole are two popular big data processing platforms used in the industry. In this comparison, we will highlight key differences between Apache Spark and Qubole to help users determine which platform suits their specific needs.

1. **Deployment**: Apache Spark is an open-source distributed computing system that can be deployed on-premises, on cloud platforms like AWS, or in a hybrid environment. Qubole, on the other hand, is a cloud-native data platform that specifically focuses on running Apache Spark, Hadoop, and other big data frameworks in the cloud. 
2. **Managed Services**: Qubole provides a fully managed service for running Apache Spark clusters, handling tasks like cluster provisioning, autoscaling, and maintenance, making it easier for users to focus on data processing tasks. In contrast, with Apache Spark, users are responsible for managing their clusters, which requires more expertise and effort.
3. **Cost**: Apache Spark is open source and free to use, but users need to bear the costs of managing their infrastructure. Qubole, being a managed service, charges a fee based on the resources and usage, making it a cost-effective option for organizations looking to leverage big data technologies without the overhead of infrastructure management.
4. **Integration**: Apache Spark can be integrated with various data sources and third-party libraries for data processing tasks. Qubole, in addition to Apache Spark, offers seamless integration with other big data tools, storage platforms, and data visualization tools, making it a comprehensive solution for data processing and analytics.
5. **Security**: Both Apache Spark and Qubole provide robust security features, including encryption, access control, and compliance certifications. However, Qubole offers additional security layers, such as data isolation and fine-grained access controls, to ensure data privacy and compliance in multi-tenant cloud environments.
6. **Scalability**: Apache Spark is known for its scalability, enabling users to process massive amounts of data efficiently. Qubole's cloud-native architecture further enhances scalability by providing auto-scaling capabilities and seamless integration with cloud resources, allowing users to scale their data processing workloads dynamically based on demand.

In Summary, Apache Spark and Qubole have key differences in deployment options, managed services, cost structure, integration capabilities, security features, and scalability, catering to different needs of users in big data processing and analytics.

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Advice on Qubole, 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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Detailed Comparison

Qubole	Apache Spark
Qubole is a cloud based service that makes big data easy for analysts and data engineers.	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.
Intuitive GUI;Optimized Hive;Improved S3 Performance;Auto Scaling;Spot Instance Pricing;Managed Clusters;Cloud Integration;Cluster Lifecycle Management	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 36	Stacks 3.1K
Followers 104	Followers 3.5K
Votes 67	Votes 140
Pros & Cons
Pros 13 Simple UI and autoscaling clusters 10 Feature to use AWS Spot pricing 7 Real-time data insights through Spark Notebook 7 Optimized Spark, Hive, Presto, Hadoop 2, HBase clusters 6 Easy to manage costs	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
Google Compute Engine Microsoft Azure	No integrations available

What are some alternatives to Qubole, Apache Spark?

Google BigQuery

Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

Amazon Redshift

It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

Presto

Distributed SQL Query Engine for Big Data

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

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.

Altiscale

we run Apache Hadoop for you. We not only deploy Hadoop, we monitor, manage, fix, and update it for you. Then we take it a step further: We monitor your jobs, notify you when something’s wrong with them, and can help with tuning.

Snowflake

Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.

Related Comparisons

Apache Spark vs Qubole: What are the differences?

## Introduction
Apache Spark and Qubole are two popular big data processing platforms used in the industry. In this comparison, we will highlight key differences between Apache Spark and Qubole to help users determine which platform suits their specific needs.

1. **Deployment**: Apache Spark is an open-source distributed computing system that can be deployed on-premises, on cloud platforms like AWS, or in a hybrid environment. Qubole, on the other hand, is a cloud-native data platform that specifically focuses on running Apache Spark, Hadoop, and other big data frameworks in the cloud. 
2. **Managed Services**: Qubole provides a fully managed service for running Apache Spark clusters, handling tasks like cluster provisioning, autoscaling, and maintenance, making it easier for users to focus on data processing tasks. In contrast, with Apache Spark, users are responsible for managing their clusters, which requires more expertise and effort.
3. **Cost**: Apache Spark is open source and free to use, but users need to bear the costs of managing their infrastructure. Qubole, being a managed service, charges a fee based on the resources and usage, making it a cost-effective option for organizations looking to leverage big data technologies without the overhead of infrastructure management.
4. **Integration**: Apache Spark can be integrated with various data sources and third-party libraries for data processing tasks. Qubole, in addition to Apache Spark, offers seamless integration with other big data tools, storage platforms, and data visualization tools, making it a comprehensive solution for data processing and analytics.
5. **Security**: Both Apache Spark and Qubole provide robust security features, including encryption, access control, and compliance certifications. However, Qubole offers additional security layers, such as data isolation and fine-grained access controls, to ensure data privacy and compliance in multi-tenant cloud environments.
6. **Scalability**: Apache Spark is known for its scalability, enabling users to process massive amounts of data efficiently. Qubole's cloud-native architecture further enhances scalability by providing auto-scaling capabilities and seamless integration with cloud resources, allowing users to scale their data processing workloads dynamically based on demand.

Apache Spark vs Qubole

Overview