Cloudera vs Pivotal

May 25, 2023 | Author: Michael Stromann
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Cloudera
Cloudera helps you become information-driven by leveraging the best of the open source community with the enterprise capabilities you need to succeed with Apache Hadoop in your organization. Designed specifically for mission-critical environments, Cloudera Enterprise includes CDH, the world’s most popular open source Hadoop-based platform, as well as advanced system management and data management tools plus dedicated support and community advocacy from our world-class team of Hadoop developers and experts. Cloudera is your partner on the path to big data.
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Pivotal
Pivotal is the leading enterprise PaaS, powered by Cloud Foundry. It delivers an always-available, turnkey experience for scaling and updating PaaS on the private cloud. Pivotal is enabling the creation of modern software applications that leverage big & fast data – on a single, cloud independent platform.
Cloudera and Pivotal are both prominent companies in the field of big data and analytics, but they offer different approaches and solutions. Cloudera is known for its expertise in providing enterprise-grade data management and analytics solutions based on Apache Hadoop. They offer a comprehensive platform that enables organizations to store, process, and analyze large volumes of data using various tools and technologies within the Hadoop ecosystem. Cloudera's platform focuses on scalability, reliability, and security, providing a robust foundation for data-driven insights.

On the other hand, Pivotal takes a broader approach by offering a cloud-native platform that combines big data analytics, application development, and cloud services. Their flagship product, Pivotal Cloud Foundry, is an open-source platform that allows developers to build, deploy, and manage applications in a cloud environment. Pivotal also provides data analytics capabilities through their Pivotal Greenplum and Pivotal GemFire offerings, allowing organizations to process and analyze large datasets for actionable insights.

See also: Top 10 Public Cloud Platforms
Cloudera vs Pivotal in our news:

2022. Cloudera launches its all-in-one SaaS data lakehouse



Cloudera, the company that specializes in big data with a focus on Hadoop, is now shifting its focus towards becoming the unified data fabric for hybrid data platforms. Taking a step further in this direction, the company recently launched its Cloudera Data Platform (CDP) One, a data lakehouse as a service (LaaS). This managed offering aims to provide enterprises with a platform that enables self-service analytics and data access for a broader range of employees. While Databricks, known for popularizing the lakehouse concept, also offers SaaS-based solutions, Cloudera positions its service as the "first all-in-one data lakehouse SaaS offering." Cloudera emphasizes that its service combines compute, storage, machine learning, streaming analytics, and enterprise security, making it a comprehensive solution for organizations.


2019. VMware completes $2.7 billion Pivotal acquisition



VMware has successfully completed the $2.7 billion acquisition of Pivotal, a private cloud platform. This acquisition serves as another crucial step for VMware as it strives to transition from solely a virtual machine company to a cloud-native provider capable of managing infrastructure across various environments. This addition aligns with VMware's recent acquisitions of Heptio and Bitnami, further reinforcing its strategic vision. The ultimate goal is to seamlessly integrate these developments into VMware Tanzu, a comprehensive management platform designed to unify Kubernetes containers and VMware virtual machines.


2018. Big Data platforms Cloudera and Hortonworks merge



Over time, Hadoop, the once-prominent open-source platform, fostered the growth of numerous companies and an ecosystem of vendors. However, the complexity associated with Hadoop posed a significant challenge. This is where companies like Hortonworks and Cloudera stepped in, offering packaged solutions for IT departments seeking the advantages of a big data processing platform without the need to build Hadoop from scratch. These companies provided various approaches to tackle the complexity, but as cloud-based big data solutions gained prominence, the notion of implementing a Hadoop system from scratch became less compelling, even with the assistance of firms like Cloudera and Hortonworks. Today, both companies have announced their merger in a deal valued at $5.2 billion. The combined entity will serve a customer base of 2,500, generate $720 million in revenue, and possess $500 million in cash reserves, all while remaining debt-free.


2015. Hortonworks acquired dataflow solutions developer Onyara



Hortonworks, a publicly traded company that offers a commercial distribution of the open-source big data software Hadoop, has announced its acquisition of Onyara, an early-stage startup known for the development of Apache NiFi. This open-source software originated within the National Security Agency (NSA) and enables efficient delivery of sensor data to appropriate systems while maintaining data tracking capabilities. In addition to previous acquisitions like XA Secure and SequenceIQ, Hortonworks has now expanded its portfolio with the intention of introducing a new subscription service based on Apache NiFi. This subscription will be marketed under the name Hortonworks DataFlow.


2015. Google partners with Cloudera to bring Cloud Dataflow to Apache Spark



Google has announced a collaboration with Cloudera, the Hadoop specialists, to integrate its Cloud Dataflow programming model into Apache's Spark data processing engine. By bringing Cloud Dataflow to Spark, developers gain the ability to create and monitor data processing pipelines without the need to manage the underlying data processing cluster. This service originated from Google's internal tools for processing large datasets at a massive scale on the internet. However, not all data processing tasks are identical, and sometimes it becomes necessary to run tasks in different environments such as the cloud, on-premises, or on various processing engines. With Cloud Dataflow, data analysts can utilize the same system to create pipelines, regardless of the underlying architecture they choose to deploy them on.


2014. Enterprise Hadoop provider Hortonworks filed for an IPO



Hortonworks, the company developing commercial Hadoop technology, has submitted its initial public offering (IPO) filing. With over $33 million in revenue and an operating loss of nearly $88 million, the company has showcased its financial performance for the current year. Hortonworks emerged as a separate entity from Yahoo in 2011 and provides a comprehensive big data processing platform. This platform enables the processing of diverse data types, including SQL and NoSQL sources, and facilitates data search and visualization using various analytics tools. Hortonworks is renowned for its exclusive focus on Hadoop, offering a solution devoid of any proprietary extensions.


2014. Cloudera helps to manage Hadoop on Amazon cloud



Hadoop vendor Cloudera has unveiled a new offering named Director, aimed at simplifying the management of Hadoop clusters on the Amazon Web Services (AWS) cloud. Clarke Patterson, Senior Director of Product Marketing, acknowledged the challenges faced by customers in managing Hadoop clusters while maintaining extensive capabilities. He emphasized that there is no difference between the cloud version and the on-premises version of the software. However, the Director interface has been specifically designed to be self-service, incorporating cloud-specific features like instance-tracking. This enables administrators to monitor the cost associated with each cloud instance, ensuring better cost management.


2014. Cloudera bought data-visualization startup DataPad



Cloudera, a cloud-based big data platform, has acquired DataPad, a data-visualization startup specializing in Python-based data analysis. This move by Cloudera is aimed at strengthening its Python tooling to attract more data scientists and developers, given the increasing competition in the Hadoop market. The co-founders of DataPad, who are well-known in the data science community for their development of the Python-based data analysis library Pandas, make this acquisition even more significant. In the commercial Hadoop market, where billions of dollars are at stake, companies like Cloudera, Hortonworks, MapR, and Pivotal are all vying to capture as many users as possible for their Hadoop distributions and big data infrastructure. Expanding the user base beyond IT staff and systems architects to include application developers and data analysts within the company is an effective strategy to ensure widespread adoption of their offerings.


2014. Pivotal brings its cloud services to Mobile



Pivotal, the enterprise cloud platform, is introducing new services specifically tailored for mobile development, which complement the company's previously announced data services. The newly launched CF Mobile Service encompasses push notifications, an API gateway, and data-sync services. This service also provides IT departments with the ability to establish distinct policies and service level agreements to ensure that data remains within the enterprise's control. Pivotal CF is built upon the Cloud Foundry PaaS, which is an open-source platform. As a result, Pivotal CF supports a wide array of open-source tools, including various databases such as MongoDB, Riak, Apache Cassandra, and the Neo4j graph database, reflecting its rich heritage.


2014. GE becomes Big Data provider



In the previous year, General Electric (GE) embarked on a significant investment of $105 million in the Big Data platform known as Pivotal. Currently, GE is actively implementing the powerful big data analytics capabilities provided by Pivotal, both internally and for customers who purchase its jet engines. To establish its analytics infrastructure, GE utilized Pivotal's Big Data Suite in conjunction with EMC's appliances, initially focusing on its aviation group. Within a span of 90 days, GE successfully established this capability and expanded its usage to 25 airline customers, granting them access to extensive data and analytics resources. Through the aggregation of data from approximately 15,000 flights, GE acquired 14 GB of information per flight, which could be efficiently analyzed within a reasonable timeframe. By employing traditional methods, processing the required data to identify a maintenance issue would have taken approximately 30 days. However, with the integration of Pivotal's technology, GE can now conduct substantial analytical operations in just 20 minutes.

Author: Michael Stromann
Michael is an expert in IT Service Management, IT Security and software development. With his extensive experience as a software developer and active involvement in multiple ERP implementation projects, Michael brings a wealth of practical knowledge to his writings. Having previously worked at SAP, he has honed his expertise and gained a deep understanding of software development and implementation processes. Currently, as a freelance developer, Michael continues to contribute to the IT community by sharing his insights through guest articles published on several IT portals. You can contact Michael by email stromann@liventerprise.com