Top Cloudera Alternatives & Competitors 2022

There are a number of viable Cloudera alternatives available on the market today. While Cloudera has been the dominant player in the Hadoop-based data analytics space for some time, recent years have seen the rise of several competitors offering compelling alternative solutions. In this article, we’ll take a look at three of the most popular Cloudera alternatives and compare their features and pricing.

Competitors and Alternatives to Cloudera

  • Amazon Web Services (AWS)
  • Oracle.
  • IBM.
  • Microsoft.
  • Google.
  • Databricks.
  • Teradata.
  • MongoDB.

There are many alternatives to Cloudera’s Hadoop platform. Some of the most popular include Apache Hadoop, Hortonworks Data Platform (HDP), and MapR. Each has its own strengths and weaknesses, so it’s important to evaluate your needs before choosing a platform.

If you’re looking for an alternative to Cloudera, here are some things to keep in mind: -Does the platform support all of the features you need? -Is it compatible with your existing infrastructure?

-How easy is it to use? -What is the cost? Apache Hadoop is one of the most popular alternatives to Cloudera.

It’s open source, so it’s often more affordable than other platforms. Additionally, Apache Hadoop supports a wide range of features and can be easily integrated into existing infrastructure. However, it can be more difficult to use than some of the other options on this list.

Hortonworks Data Platform (HDP) is another popular option for those looking for an alternative to Cloudera. HDP is built on top of Apache Hadoop and includes additional management tools and security features. It’s also compatible with a wide range of systems and devices.

However, HDP can be more expensive than some of the other options on this list. MapR is a commercial alternative to Cloudera that offers many of the same features as HDP. It also includes additional management tools and security features.

MapR is easy to use and compatible with a wide range of systems and devices.

Cloudera Alternatives Open Source

There are a few other great open source alternatives to Cloudera. Here are three of the best:

1. Apache Hadoop

This is the original big data platform and it’s still going strong. Hadoop is highly scalable and can handle huge amounts of data with ease. It’s also very flexible, allowing you to process data in a variety of ways.

2. Hortonworks Data Platform (HDP)

HDP is based on Apache Hadoop and adds a number of useful features and enhancements. It’s a great option if you’re looking for a more feature-rich platform than plain vanilla Hadoop.

3. MapR Converged Data Platform

MapR takes a different approach to big data, providing a converged platform that includes both Hadoop and Spark. This makes it easy to get started with big data processing, while still giving you the flexibility to scale up as needed.

Cloudera Stock

Cloudera is a data platform that offers users the ability to store, process, and analyze data using Hadoop. The company went public in April of 2017, and its stock is traded on the NASDAQ under the ticker symbol CLDR. As of June 2018, Cloudera’s market capitalization was $4.21 billion.

The company was founded in 2008 by Amr Awadallah, Jeff Hammerbacher, and Mike Olson. All three men were early employees at Google (Awadallah was one of the first 100 employees), and they saw firsthand how powerful Google’s data processing infrastructure was. They decided to build a similar platform that would be open source and available to anyone who wanted to use it.

Hadoop is a software framework that allows for distributed storage and processing of large data sets across clusters of computers. It is designed to scale up from a single server to thousands of machines, each offering local computation and storage. Cloudera offers a distribution of Hadoop that includes additional tools for management, security, and ysis.

The company also provides support and services for its distribution. Cloudera’s customers include some of the largest organizations in the world, such as AOL, Cisco, eBay, Facebook, Yahoo!, and Zynga.

Cloudera Vs Snowflake

If you’re considering a move to the cloud for your data management needs, you may be wondering whether Cloudera or Snowflake is the right platform for you. Both offer significant advantages in terms of flexibility, scalability, and cost-effectiveness, so it can be tough to decide which one is best for your particular situation. To help you make an informed decision, we’ve put together a side-by-side comparison of Cloudera and Snowflake.

Read on to learn more about each platform’s features, pricing model, and strengths. Cloudera: An Overview Cloudera is an enterprise data management platform that enables users to store, process, and analyze data in the cloud.

It offers a variety of features designed to make data management easier and more efficient, including: A unified interface for all tasks: With Cloudera’s unified interface, users can easily access all the tools they need from one central location. This makes it easy to manage large amounts of data and eliminates the need for multiple point solutions.

Flexible storage options: Cloudera offers both on-premises and cloud-based storage options. This gives users the flexibility to choose the option that best meets their needs. Scalability: Cloudera’s platform is highly scalable, making it easy to add additional capacity as needed.

This helps businesses avoid overprovisioning costs and ensures they have the resources they need to support future growth. Snowflake: An Overview Snowflake is a cloud-based data warehouse service that offers instant elasticity, secure data sharing across accounts, and per-second pricing .

It also includes a number of innovative features designed to make working with data easier and more efficient, such as: Zero copy clones : Snowflake’s unique zero copy clones feature allows users to create copies of their data without incurring any additional storage costs . This makes it easy to test new queries or analytics without affecting production workloads .

Time travel : Snowflake’s time travel feature lets users query past versions of their data , which can be useful for auditing purposes or investigating anomalies . Flexible deployment : Snowflake can be deployed on premises or in the cloud , giving users the flexibility to choose the option that best meets their needs .

Cloudera Vs Databricks

There are a few key differences between Cloudera and Databricks that are worth mentioning. For one, Cloudera is an enterprise data management platform while Databricks is more focused on data analytics and processing. This means that Cloudera offers a wider range of features than Databricks, including support for things like batch processing, streaming data, and security.

However, it also means that Cloudera can be more expensive and complex to use than Databricks. Another difference between the two platforms is how they handle scalability. Cloudera uses a traditional scaling approach where you add more nodes to your cluster as your needs grow.

Databricks takes a different approach called autoscaling which automatically scales your cluster up or down based on demand. This can make Databricks more cost-effective since you only pay for the resources you use, but it can also be less predictable since your cluster size can change unexpectedly. Finally, another key difference is in the licensing model.

Cloudera uses a per-node licensing model while Databricks offers a subscription-based pricing model. This means that with Cloudera, you’ll need to purchase a license for each node in your cluster (which can get expensive) while with Databricks you’ll just pay a monthly fee regardless of how many nodes you use.

Ambari Alternatives

There are a few different options for managing Hadoop clusters, each with its own advantages and disadvantages. Here is a brief overview of some of the most popular Ambari alternatives:

1. Cloudera Manager

Cloudera Manager is a comprehensive management tool designed specifically for Hadoop clusters. It includes features such as role-based access control, granular activity monitoring, and support for multiple Hadoop distributions. One advantage of Cloudera Manager is that it can be used to manage both on-premise and cloud-based Hadoop deployments.

2. Hortonworks Data Platform (HDP)

HDP is an enterprise-grade Hadoop platform that includes all the core components necessary for running production workloads at scale.

Hortonworks offers a number of management tools specifically designed for HDP, including the Ambari-based Hortonworks Data Management Platform (DMP). DMP provides a centralized view of the entire HDP deployment and makes it easy to provision, monitor, and manage HDP clusters.

3. MapR Converged Data Platform

MapR Converged Data Platform (MapR CDP) combines the power of Apache Hadoop with Enterprise File System (EFS) capabilities to provide a complete data management solution in one platform.

MapR CDP also includes several powerful management tools, such as the MapR Control System (MCS), which provides a single point of control for provisioning, monitoring, and managing MapR CDP deployments.

Cloudera Alternatives


Who is Competitor to Cloudera?

There are a few companies that compete with Cloudera for the Hadoop market. Hortonworks, MapR, and Amazon EMR are all major competitors in the space. Hortonworks is one of the original founders of the Hadoop ecosystem and offers an enterprise-grade platform called HDP (Hortonworks Data Platform).

Hortonworks has been working closely with Microsoft to offer HDP on Azure and also recently announced a new partnership with Google Cloud Platform. MapR is another big player in the Hadoop market and offers a comprehensive data platform that supports a wide variety of workloads including batch processing, streaming, real-time analytics, and machine learning. MapR also has partnerships with Google Cloud Platform and Microsoft Azure.

Amazon EMR is Amazon’s managed Hadoop offering that runs on Amazon Elastic Compute Cloud (EC2) and Amazon Simple Storage Service (S3). Amazon EMR makes it easy to set up, operate, and scale Hadoop clusters and provides tight integration with other AWS services such as Amazon DynamoDB and Amazon Kinesis.

How is Snowflake Different from Cloudera?

Snowflake is a data warehouse-as-a-service offering from Snowflake Computing. It offers all the benefits of traditional data warehouses, but with none of the drawbacks. Snowflake is built on top of Amazon Web Services (AWS) and uses a unique architecture that separates storage, compute, and networking so that each can be scaled independently.

This approach enables Snowflake to offer an unprecedented level of flexibility, performance, and concurrency. Cloudera is an open source platform that includes Apache Hadoop and several related projects. Cloudera offers a Distribution Including Apache Hadoop (CDH), which is one of the most popular distributions of Hadoop in the world.

CDH provides everything you need to get started with Hadoop, including MapReduce, HDFS, YARN, Hive, Impala, Spark, Oozie etc. Cloudera also offers enterprise support for its customers. So how exactly is Snowflake different from Cloudera?

Let’s take a closer look: Architecture: As mentioned earlier, Snowflake has a unique architecture that separates storage, compute and networking so that each can be scaled independently. This gives users much more control over their resources and allows them to scale up or down as needed without incurring any downtime or performance issues.

In contrast, Cloudera’s architecture is based on the traditional shared-nothing model where all nodes in the cluster are equal peers with no single point of failure.


Because of its innovative architecture, Snowflake is able to offer true horizontal scalability – meaning it can scale out infinitely by adding more nodes to the cluster. Cloudera’s scalability is limited by the shared-nothing architecture; at some point you will reach the maximum capacity for your cluster size and will have to either remove nodes or add more clusters if you want to continue scaling outwards.


The separation of storage and compute in Snowflake’s architecture also leads to much better performance than what’s possible with Cloudera..

With Snowflake there is no need to move data around between nodes in order to process it; data can simply be read directly from storage into memory where it can be quickly processed by the compute layer.. This greatly reduces processing times and makes it possible to run complex queries on large datasets that would otherwise be too slow with other platforms..

Is Cloudera a Competitor to Aws?

Cloudera is a leading distribution of the Apache Hadoop project. It offers an easy to use platform for big data processing, warehousing and analytics. Cloudera’s products include CDH (Cloudera Distribution Including Apache Hadoop) and Cloudera Manager.

AWS does not have a direct competitor to Cloudera but there are several distributions of Hadoop available on AWS including Amazon EMR, Hortonworks, MapR and IBM BigInsights.

What Will Replace Hadoop?

The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of commodity hardware nodes.

Hadoop was created by Doug Cutting and Mike Cafarella in 2005. All the core components of Hadoop are available under the Apache v2 license. The project includes these modules:

  • MapReduce: A programming model and execution environment for large-scale data processing.
  • HDFS: A scalable distributed file system that handles large amounts of data with streaming access patterns, running on commodity hardware.
  • YARN: Yet Another Resource Negotiator, introduced in Hadoop 2.0, which enables MapReduce applications to run on a cluster without depending on any particular DataNode (slave node) for its execution – thus providing true scalability and increased resource utilization efficiency within an organization’s Hadoop infrastructure investment.

These three modules form the core of what is commonly referred to as “the Hadoop platform” or more simply “Hadoop”. Other popularly used Big Data frameworks include Storm, Spark and Flink but their functionalities differ from those found in Hadoop (e.g., while MapReduce can be used with both batch and real-time data analytics workloads, Storm is focused solely on real-time stream processing). There are two main reasons why some organizations are looking into alternatives or replacements for Hadoop: first, due to its complexity which makes it hard to set up, configure and maintain; second – even though YARN has increased its flexibility – some argue that it still lacks support for certain types of workloads (e.g., graph processing) or use cases (e.. Internet of Things/Sensor Data).

Some have even gone as far as saying that “there is no future for Hadoop”. While this may be too harsh an assessment given that Hortonworks – one commercial distribution vendor – recently went public, it does beg the question: what will replace Hadoop?

Why Hadoop is Dying


There are many companies that offer alternatives to Cloudera’s Hadoop distributions. Some of these companies include Hortonworks, MapR, and Amazon EMR. Hortonworks is a company that offers an alternative to Cloudera’s Hadoop distribution.

Hortonworks’ distribution is called HDP (Hortonworks Data Platform). HDP includes all of the core Apache Hadoop projects, as well as additional projects from the Hortonworks community. MapR is another company that offers an alternative to Cloudera’s Hadoop distribution.

MapR’s distribution is called the MapR Converged Data Platform (MapR-CDP). MapR-CDP includes all of the core Apache Hadoop projects, as well as additional projects from the MapR ecosystem. Amazon EMR is a cloud-based service that provides a managed Hadoop framework.

Amazon EMR uses Amazon EC2 instances for compute resources and S3 for storage. Amazon EMR supports a variety of applications, including Hive, Pig, Spark, and Presto.

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