Cloudera Data Engineering Reviews

Cloudera Data Engineering provides a solid environment for building and managing data pipelines at scale.

I like the way it integrates with Apache Spark and Airflow, making batch processing and scheduling efficient

Initial setup and configuration can be complex, especially in hybrid cloud environments.

.Easy to use and support provided by cloudera

Since its best priduct hence Nothing to highlight about dislikes

To get insight from data

1. The Best thing about Cloudera Data Engineering is its security, it's built for a high scala -enterprise-level data engineering activities.

2. It is user-friendly, which eases the integration of code in case of frequent usage.

3. Customer Care Service is up to the mark when it comes to addressing the issue.

4. It is a platform, built on Apache Spark, with tools such as Apache Airflow for automation, orchestration & monitoring makes it an easily usable tool that too with basic implementation.

1)The thing I love about Cloudera Data Engineering is that it is secure, built for a high scala -entreprise level data engineering activities

2)It is an integrated all inclusive data engineering platform built upon Apache Spark,with tools such as Apache airflow for automation, orchestration and monitoring making it a quite easy to use tool with easy implementation.

3) Customer care service is great across using the platform.

4) Cloud service which eases integration of code, if used regularly we become more and more aware of more features

Cloudera offers robust big data solutions, empowering businesses with efficient data management and analytics. Their user-friendly platform and excellent support enable organizations to harness the power of data for smarter decisions and growth.

The data engineering course had some drawbacks, including the lack of hands-on practical exercises, outdated course materials, and limited interaction with instructors, which hindered the depth of understanding and real-world application of concepts.

Cloudera Data Engineering solves complex data management and processing challenges for businesses. By offering a scalable, integrated platform, it enables efficient data ingestion, storage, transformation, and analysis. This benefits me by streamlining data workflows, improving data quality, and enhancing decision-making, ultimately leading to greater productivity and competitiveness.

.The different integrations it allows like with spark, airflow,

Nothing just that it takes sometime to get used to the interface

Big data related problems and analysis

Easy to use , with the benefits of Apache airflow for orchestrations along with Apache spark for heavy lifting and data pipeline automation. Provides seamless integration with data warehouse and machine learning

It can be costly for organization having large amount of data. The pricing structure for enterprises can be high. Also the platform lacks the unavailability of data science and visualization interface

Writing data pipelines , with

Combining CDE with business execution engine can serve as a solid foundation for complex event processing on big data. we can handle large amount of data easily as it gives us flexiblity to scaleup the resources.

it provides alrge amount of technologies for streaming the live data and we have to pay only for what we are using.

It is only suitable for large amount of data.

Orchestrastion automation airflow is very much used in my organisation. you can dirctly monitor from tool and fix the jobs there.

Easy to understand and mostly the way it is being created.

I haven't seen marketing of cloudera the way it should required and reach to people

Mostly related to self learning.

It is easy to use and even a begineer can use cloudera data engineering platform easily.

The size of the application is very large and sometimes the system hangs because of the laod it takes.

Handling big data situations where large amount of data can not be handled by any other platform.