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Cloudera vs. Databricks: 6 Key Differences and How to Choose

Alon Berger

Product Marketing Manager, Intel Granulate

What is Cloudera? 

Cloudera is a software company that provides a platform for data analytics and machine learning, leveraging an open-source framework. It allows enterprises to efficiently capture, store, process, and analyze vast amounts of data. 

Originally built around the Hadoop ecosystem, Cloudera has evolved to include a wide range of technologies that facilitate data processing, real-time analytics, and machine learning tasks, catering to the needs of businesses aiming to derive valuable insights from their data.

Cloudera’s platform supports multiple deployment options including on-premises, in the cloud, or in a hybrid environment. This ensures that organizations can manage their data workloads and analytics operations efficiently, regardless of their size or the complexity of their data infrastructure. 

The platform’s focus on security, governance, and management further enhances its appeal to industries with stringent data compliance and privacy requirements.

Webinar Optimizing Databricks with Deloitte

What is Databricks? 

Databricks is a cloud-based data analytics platform founded by the creators of Apache Spark, a powerful open-source processing engine. Databricks integrates with Apache Spark to offer enhanced capabilities for data engineering, data science, and machine learning. 

Databricks provides a collaborative workspace that allows data scientists, engineers, and business analysts to work together seamlessly, fostering innovation and efficiency. Its unified analytics platform is designed to simplify the process of working with massive datasets and to accelerate the time-to-value for data-driven decision making. 

The service focuses on providing a high-performance environment that can handle complex analytical tasks at scale. Databricks’ platform supports a variety of cloud infrastructures, including Amazon Web Services, Microsoft Azure, and Google Cloud Platform, enabling businesses to deploy and scale data analytics solutions using the cloud provider of their choice.

In this article:

Cloudera Key Features

Automatic Configuration and Isolation

Cloudera simplifies the management of data warehouses by offering automatic configuration, ensuring that data analytics environments are set up with optimal settings from the start. This feature minimizes the need for manual adjustments and technical know-how, allowing businesses to focus more on deriving insights rather than on the setup process. 

Cloudera also provides isolation capabilities that secure data analytics operations. By isolating workloads, Cloudera ensures that different processes do not interfere with each other, maintaining both the integrity and the performance of data analytics tasks. This is especially important for enterprises handling sensitive or critical data, as it helps in enforcing data privacy and security standards.

Optimization Based on Workloads

Cloudera is engineered to optimize its operations based on the specific workloads it handles. This means that the platform automatically adjusts its resources and settings to best suit the tasks at hand, whether they involve large-scale data processing or intricate analytics. 

This optimization leads to enhanced performance, as Cloudera can dynamically allocate resources where they are most needed, ensuring that data processing and analytics jobs run efficiently. This capability not only improves speed and reliability but also contributes to cost efficiency, as resources are utilized in the most effective manner possible.

Auto-Suspend and Resume

A key feature of Cloudera is its ability to automatically suspend and resume operations, which contributes significantly to resource efficiency and cost savings. When a data analytics operation is not actively being used, Cloudera can suspend it, thereby freeing up resources and reducing costs associated with running the operation. 

Once the operation is needed again, Cloudera resumes it, ensuring that there is minimal delay in accessing data and analytics tools. This auto-suspend and resume functionality is particularly beneficial for businesses that have variable analytics needs, as it allows them to scale their operations up or down without manual intervention.

Security Support

Security is a cornerstone of Cloudera’s platform, offering robust data protection at every stage of the analytics process. Cloudera incorporates advanced security features that include encryption, access control, and continuous monitoring to safeguard data against unauthorized access and potential breaches. 

These security measures are integrated into the platform’s architecture, ensuring that data is protected without compromising on performance. For industries that deal with highly sensitive information, such as finance and healthcare, Cloudera’s comprehensive security support provides the assurance that their data analytics operations are secure and compliant with regulatory standards.

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Databricks Key Features

Data Engineering

Databricks excels in data engineering capabilities, offering a robust platform for processing and transforming massive datasets efficiently. With its tight integration with Apache Spark, Databricks allows data engineers to leverage high-performance processing to clean, aggregate, and prepare data for analytics or machine learning applications. 

This capability is crucial for organizations dealing with vast volumes of data, enabling them to streamline their data pipelines and ensure that data is accurate and ready for analysis.

Data Analytics 

Databricks provides an advanced data analytics environment that enables businesses to explore and analyze their data with greater speed and flexibility. Its collaborative workspace fosters teamwork among data scientists, analysts, and business stakeholders, facilitating the exchange of ideas and the development of insights. 

With Databricks, users can quickly perform complex analyses and visualize their results, accelerating the decision-making process and driving business value from their data.

Machine Learning

The Databricks platform is particularly noted for its machine learning capabilities, offering a comprehensive suite of tools and services that streamline the development and deployment of machine learning models. 

Databricks simplifies the machine learning lifecycle, from data preparation and model training to deployment and monitoring, enabling data scientists to focus on creating value rather than managing infrastructure.

Collaborative Workspace

One of the standout features of Databricks is its collaborative workspace, designed to bring together data scientists, engineers, and analysts in a shared environment. This collaboration fosters innovation and accelerates project timelines, as team members can easily share insights, models, and analyses. 

The workspace supports various programming languages and integrates seamlessly with popular data science tools, making it suitableit an suitable for interdisciplinary teams working on complex data projects.

Databricks vs. Cloudera: Key Differences

1. Target Audience and Use Cases

Databricks and Cloudera cater to slightly different audiences and use cases, reflecting their distinct approaches to data analytics and machine learning. Databricks is particularly well-suited for organizations focused on advanced analytics, real-time data processing, and collaborative data science projects. Its platform is designed to enable rapid innovation and experimentation.

Cloudera targets enterprises that require a comprehensive data management solution with strong security, governance, and multi-cloud capabilities. Its platform is suitable for organizations looking to leverage big data for strategic insights, ensuring data compliance and supporting complex data workflows across various deployment environments. 

Webinar Optimizing Databricks with Deloitte

2. Scalability and Flexibility

Databricks excels in providing a scalable and flexible cloud-based analytics platform that can dynamically adjust resources to meet the demands of data processing and analytics tasks. Its architecture is designed to optimize performance and cost-efficiency, allowing businesses to scale up or down as needed without significant upfront investment. 

Cloudera, while also offering a scalable solution, places a greater emphasis on providing a flexible deployment model that supports on-premises, cloud, and hybrid environments. This flexibility ensures that organizations can tailor their data architecture to meet specific operational, regulatory, and business requirements. 

3. Data Processing and Analytics

Databricks leverages the power of Apache Spark to provide fast and efficient data processing, enabling real-time analytics and machine learning on large datasets. Its optimized execution engine and intelligent data processing features ensure high performance, making it appropriate for organizations requiring advanced analytics capabilities and real-time insights.

Cloudera, with its roots in the Hadoop ecosystem, provides a broad range of data processing and analytics tools that support batch and real-time data workflows. Its platform integrates with various open-source projects, including Apache Hadoop, Apache Spark, and Apache Kafka, offering a versatile solution for processing diverse data types and volumes.

4. Machine Learning and AI Capabilities

Databricks provides a collaborative environment for developing and deploying machine learning models, with features like MLflow for experiment tracking and model management. The platform is optimized for machine learning workflows, enabling data scientists to build, train, and deploy models efficiently, accelerating the path from experimentation to production.

Cloudera also offers machine learning and AI tools through its Cloudera Data Science Workbench, enabling data scientists to build and deploy models using their preferred languages and frameworks. While the platform supports machine learning workflows, it is designed with a focus on integrating machine learning capabilities into broader data management and analytics operations. 

5. Deployment Options

Databricks is a cloud-native platform that supports multiple cloud environments, including AWS, Azure, and Google Cloud. This focus on cloud deployments enables rapid scalability and flexibility, allowing organizations to leverage cloud infrastructure for their data analytics and machine learning needs. Databricks’ architecture is designed for organizations that prioritize agility and wish to avoid the complexities of managing on-premises infrastructure.

Cloudera, in contrast, offers a wider range of deployment options, including on-premises, cloud, and hybrid models. This flexibility caters to enterprises with diverse operational and regulatory requirements. The platform is particularly suited to organizations that require tight control over their data environment, including industries with strict data sovereignty and security regulations. 

6. Data Governance and Security

Databricks provides robust data security and governance, incorporating advanced security features and compliance certifications to protect data and support regulatory compliance. It offers fine-grained access controls, encryption, and audit logs, ensuring data is secure throughout its lifecycle. Databricks’ governance capabilities, including data cataloging and lineage tracking, support data management and compliance with data protection laws. 

Cloudera, which has a long track record of use by large organizations, places a strong emphasis on data security and governance, offering comprehensive features to ensure data protection, privacy, and compliance. Its platform includes robust security measures such as encryption, access control, and audit capabilities, as well as tools for data lineage, metadata management, and policy enforcement.

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Databricks vs. Cloudera: How to Choose?

Choosing between Databricks and Cloudera depends on your organization’s specific data analytics needs, scalability requirements, and deployment preferences:

Assess Your Use Cases and Requirements

  • If your organization focuses on real-time analytics, advanced data science, and machine learning projects, Databricks might be more suitable due to its optimized performance and collaborative features.
  • For comprehensive data management with a focus on security, governance, and support for a wide range of data processing and analytics tools, Cloudera could be a better fit.

Consider Scalability and Flexibility Needs

  • Databricks offers a cloud-native, scalable solution that dynamically adjusts resources, ideal for businesses that require flexibility and high performance without substantial upfront investment.
  • Cloudera provides flexible deployment options (on-premises, cloud, hybrid) that can be tailored to specific operational, regulatory, and business needs, beneficial for organizations looking for control over their data environment.

Evaluate Data Processing and Analytics Capabilities

  • Databricks, with its integration of Apache Spark, is geared towards organizations needing efficient real-time data processing and analytics.
  • Cloudera’s broad range of data processing and analytics tools supports both batch and real-time workflows, suitable for enterprises that work with diverse data types and volumes.

Machine Learning and AI Integration

  • For a collaborative environment optimized for developing and deploying machine learning models quickly, Databricks provides advanced tools and a streamlined workflow.
  • Cloudera caters to integrating machine learning into broader data management and analytics operations, offering flexibility in tool choice and deployment.

Deployment Options

  • Organizations prioritizing cloud deployments for agility and scalability might prefer Databricks’ cloud-native approach.
  • Enterprises with specific operational, regulatory, or business requirements that necessitate a mix of on-premises, cloud, or hybrid environments may find Cloudera’s flexible deployment models more appealing.

Data Governance and Security

  • Databricks offers robust security and governance features for organizations needing to protect data and comply with regulations, with a focus on cloud environments.
  • Cloudera is known for its comprehensive data security and governance, ideal for industries with strict data protection and privacy regulations, offering features that ensure data integrity and compliance across all deployment models.
Webinar Optimizing Databricks with Deloitte

Optimizing Databricks and Cloudera with Intel Granulate

Whether you choose Databricks, Cloudera or a combination of the two, Intel Granulate is a must-have for your tech stack. As the flagship offering in the performance pillar of Intel Software’s suite of enterprise solutions, Intel Granulate optimizes big data workloads continuously and autonomously to improve throughput, reduce processing time and cut costs.

For Databricks workloads, Intel Granulate continuously and securely optimizes large-scale applications to improve job completion time with no code changes required. Users are able to seamlessly reduce DBU utilization through a combination of autonomous optimization features including runtime optimization, dynamic capacity management, and improvements to Databricks autoscaling.

If you’re using Cloudera, then Intel Granulate can improve application performance for reduced costs and increased throughput on CDP, with a focus on security and reliability. By autonomously tuning big data workloads at scale, Cloudera users are seeing cost reductions of up to 30% with Intel Granulate, as well as significant impact on memory and CPU utilization.

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