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  5. Azure Synapse vs Cloudera Enterprise

Azure Synapse vs Cloudera Enterprise

OverviewComparisonAlternatives

Overview

Cloudera Enterprise
Cloudera Enterprise
Stacks126
Followers172
Votes5
Azure Synapse
Azure Synapse
Stacks104
Followers230
Votes10

Azure Synapse vs Cloudera Enterprise: What are the differences?

Key Differences Between Azure Synapse and Cloudera Enterprise

Azure Synapse and Cloudera Enterprise are two powerful data analytics platforms, each offering unique features and capabilities. Here are the key differences between the two:

  1. Scalability: Azure Synapse is built on the cloud infrastructure of Microsoft Azure, providing unparalleled scalability. It allows users to scale up or down resources as needed, making it suitable for both small and large-scale data processing. On the other hand, Cloudera Enterprise is an on-premises solution that requires physical hardware and has limitations on scalability compared to Azure Synapse.

  2. Integration: Azure Synapse offers seamless integration with various Azure services, such as Azure Data Lake Storage and Azure Machine Learning. This integration enables users to leverage additional features and services provided by the Azure ecosystem. In contrast, while Cloudera Enterprise supports integration with third-party tools, it may require additional configurations and customizations for seamless integration.

  3. Managed Services: Azure Synapse is a fully managed service, meaning that Microsoft takes care of infrastructure management, security, and updates. This allows users to focus more on data analysis and insights rather than managing the underlying infrastructure. Cloudera Enterprise, on the other hand, requires users to handle the infrastructure and perform regular maintenance tasks.

  4. Data Processing Engines: Azure Synapse supports both T-SQL and Apache Spark for data processing. T-SQL provides a familiar and powerful querying language for structured data, while Apache Spark enables processing of large-scale and unstructured data. In contrast, Cloudera Enterprise primarily relies on Apache Hadoop and Apache Spark for data processing.

  5. Security and Governance: Azure Synapse provides advanced security features, such as Azure Active Directory integration, role-based access control, and data encryption at rest and in transit. Additionally, it offers comprehensive governance capabilities, including data masking and auditing. Cloudera Enterprise also offers robust security and governance features but may require additional configurations and setups.

  6. Cost Structure: Azure Synapse follows a pay-as-you-go pricing model, allowing users to pay only for the resources they consume. This provides cost efficiency and flexibility for organizations with varying data processing needs. Cloudera Enterprise, being an on-premises solution, may have higher upfront costs for hardware and license fees.

In summary, Azure Synapse provides superior scalability, seamless integration with Azure services, managed services, support for multiple data processing engines, enhanced security and governance features, and a flexible cost structure. Cloudera Enterprise, on the other hand, offers more control over infrastructure and customization options but requires additional management and maintenance efforts.

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Detailed Comparison

Cloudera Enterprise
Cloudera Enterprise
Azure Synapse
Azure Synapse

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.

It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

Unified – one integrated system, bringing diverse users and application workloads to one pool of data on common infrastructure; no data movement required;Secure – perimeter security, authentication, granular authorization, and data protection;Governed – enterprise-grade data auditing, data lineage, and data discovery;Managed – native high-availability, fault-tolerance and self-healing storage, automated backup and disaster recovery, and advanced system and data management;Open – Apache-licensed open source to ensure your data and applications remain yours, and an open platform to connect with all of your existing investments in technology and skills
Complete T-SQL based analytics – Generally Available; Deeply integrated Apache Spark; Hybrid data integration; Unified user experience
Statistics
Stacks
126
Stacks
104
Followers
172
Followers
230
Votes
5
Votes
10
Pros & Cons
Pros
  • 1
    Easily management
  • 1
    Hybrid cloud
  • 1
    Multicloud
  • 1
    Scalability
  • 1
    Cheeper
Pros
  • 4
    ETL
  • 3
    Security
  • 2
    Serverless
  • 1
    Doesn't support cross database query
Cons
  • 1
    Dictionary Size Limitation - CCI
  • 1
    Concurrency

What are some alternatives to Cloudera Enterprise, Azure Synapse?

Metabase

Metabase

It is an easy way to generate charts and dashboards, ask simple ad hoc queries without using SQL, and see detailed information about rows in your Database. You can set it up in under 5 minutes, and then give yourself and others a place to ask simple questions and understand the data your application is generating.

Google BigQuery

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.

Apache Spark

Apache Spark

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.

Amazon Redshift

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.

Qubole

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Presto

Presto

Distributed SQL Query Engine for Big Data

Amazon EMR

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

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.

Superset

Superset

Superset's main goal is to make it easy to slice, dice and visualize data. It empowers users to perform analytics at the speed of thought.

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.

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