We are using Cloudera Data Platform to migrate and run our ETL processes, transferring data from multiple RDBMS to a data lake for analysis purposes. The current organization I work for is a top bank with a data lake of more than one petabyte.

Cloudera Data Platform is a perfect tool to manage such vast amounts of big data, store it properly, query it, and move it from one end to another.

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My main use case for Cloudera Data Platform is dealing with large volumes of data and primarily handling unstructured data by combining structured and unstructured data on this platform.

I use Cloudera Data Platform for handling unstructured data primarily in a healthcare company where there are many research notes, which are handwritten notes. Using this platform, we have performed PDF extraction where we store PDF data and then extract the data by performing PDF extraction using this platform. That is one use case. The second use case is mainly dealing with voice files. We store the voice files, convert voice to text, and then perform text analytics on that. It is basically dealing with call center voice files.

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My main use case for Cloudera Data Platform is data analytics and AI.

For data analytics and AI in my day-to-day work, we have a multi-source system where the data keeps coming from different source systems, from RDBMS, in tabular format, or semi-structured, or streaming data from Kafka. We process and store data in the backend ADLS, then apply business rule logic to create a golden table which is published for business or end users who consume the data for analytics. Some AI engineers develop or run that code, Python code, or LLM against those data to gain insights.

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Cloudera Data Platform on AWS was adopted as the core enterprise data platform, covering the full data lifecycle from ingestion to analytics and advanced use cases. Cloudera Data Platform was used to ingest data from multiple sources, including on-premises systems, cloud-native applications, and external SaaS platforms.

Batch ingestion pipelines were implemented using Spark on Cloudera Data Platform data engineering, while near real-time ingestion was handled through streaming components integrated with the platform.

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My main use case for Cloudera Data Platform is for data analytics and AI workload.

We have different data sources where the data is coming in tabular format or CSV, semi-structured or structured, unstructured, and some sort of Kafka streaming messages. We use to store it and then we process and transform, apply the business logic, and then make the data ready for the consumer to consume.

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Handling and processing big volumes of data is my main use case for Cloudera Data Platform.

We get the instrument data from various providers, and we process them, do reconciliation, and use Cloudera Data Platform to process it and ingest it in a structured manner which is then used by our downstream consumers.

One unique aspect about my main use case with Cloudera Data Platform involves multiple application teams building their workflows on the platform. I don't have all the insights into other aspects.

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We heavily use Cloudera Data Platform for data science activities. Various departments in the company utilize it as a sandbox for data discovery. We have multiple data pipelines running on a daily and hourly basis, along with some real-time data pipelines.

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My main use case for Cloudera Data Platform is measuring HDFS and the SQL queries in Impala to troubleshoot some error in YARN applications based on Spark, and control the reporting data between Informatica and Cloudera for transport data between the DB Oracle, Mongo DB to CDP in Impala, between HDFS.

For measuring HDFS, I use Cloudera Data Platform, specifically Cloudera Manager, to analyze small files in HDFS to reduce our number for the duration of jobs that read this file and the partition date.

I mainly use Cloudera Data Platform as part of a large-scale data processing and analytics pipeline in a hybrid cloud environment, primarily on Azure, which involves managing the YARN cluster, monitoring workloads, troubleshooting performance issues, and integrating data ingestion and transformation processes from various enterprise systems. We leverage CDP for its scalability, security, and strong integration with Looker, Informatica, Hive, and Spark.

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My main use case for Cloudera Data Platform is that I am a certified administrator. I use Cloudera Data Platform in my daily work by managing it as a whole in a Telco company. I regularly handle tasks by managing Cloudera Data Platform and being responsible for its services, which are currently up and running, and managing daily administrative tasks.

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The primary usage of Cloudera Data Platform is to offload ETL processes because it's cheaper compared to data warehouse solutions like Teradata or Oracle. Furthermore, basic reporting can be done, and some real-time processes can be managed. View full review »

My main use case for Cloudera Data Platform is to host in-house data which is sensitive and very guard-railed for compliance.

A quick specific example of the type of sensitive data I'm hosting is related to personally identifiable information as well as data which is financial and transactional in nature, and Cloudera Data Platform helps with compliance by giving us a uniform approach to this. We have implemented the compliance-based entitlements using toolkits provided by Cloudera Data Platform and have our own implementation for each region where we are hosting the data.

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The main use case for Cloudera Data Platform is to support a multi-source system with a multi-data structure. We have streaming services, Kafka services, RDBMS systems, and semi-structured data in the form of CSV and JSON files where we used to have everything in place and centralized.

Cloudera Data Platform also supports a hybrid data warehouse, which is similar to a relational database management system where business users can do query analytics, similar to a select star. Cloudera Data Platform also supports PySpark, where a user can create a data frame and then do a transformation load to perform and get insights.

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We use it for multiple domains, including oil & gas, finance (Morgan Stanley), and healthcare. We process around 186 TB of data per day for analytics purposes.

Currently, we use it for healthcare domain. 

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We primarily use the solution for data storage and processing.

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We use Hortonworks Data Platform for data management, significant data ingestion, and analytics.

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There are a lot of use cases for the Hortonworks Data Platform. We use it alongside GPFS, so most of the information we use for operational analytics is primarily on the Hortonworks Data Platform.

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We use Hortonworks as a storage platform and then we create machine learning models and do the execution using Cloudera Data Science Workbench. (Cloudera and Hortonworks merged in January of 2019.)  

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We use this solution for the hospitality industry. 

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We use this solution to look at and manage big data. It's mostly historical data that we offload from our data warehouse, as well as from other databases in other platforms.

We have two different installations. The first one is based on IBM POWER CPUs, and the other one is based on Intel CPUs. Our data center is on-premise. There is some thought on moving to a private could, or a private IBM cloud, but we have not proceeded with that as of yet.

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Hortonworks actually provides a complete solution and just one user interface that can manage all the packages. It can monitor all the requirements, all the versions and additionally all the quays and all the hardware-dependent services. What I want is a useful user interface which is the reason why I currently prefer to use Hortonworks.

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We use it for data science activities.

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