Cloudera Data Lineage

What do you like best about the product?

What I like most about Cloudera is how it makes working with large volumes of data feel more manageable. Having multiple tools available within a single platform is really helpful, especially when I’m dealing with data coming from different sources. I also appreciate its integration with Apache Spark, Kafka, and other big data tools, since it makes it easier to build and connect data pipelines.

What do you dislike about the product?

One area I think could be improved is the user interface. Some sections aren’t very intuitive to navigate, especially when you’re using the platform for the first time. Setting up configurations and managing permissions can also feel a bit complicated, and it takes some time to figure out where everything is.

What problems is the product solving and how is that benefiting you?

Cloudera helps bring data from different sources together, making it easier to process and analyze everything in a single environment. Rather than managing separate tools for storage, processing, and monitoring, teams can take care of many of these tasks within the same platform, which simplifies day-to-day work and keeps things more organized.

What do you like best about the product?

To analyze distributed, high-dimensional matrices, work with large feature stores, and benchmark our models’ performance through pipelines, we need infrastructure that won’t collapse under heavy analytical throughput. The Cloudera Manager web interface is laid out in a clear, convenient way, so our teams can quickly check active nodes in the cluster, memory allocation, and running jobs without getting lost in terminal flags.

Integration between our computation notebooks, shared object storage buckets, and visualization engines is straightforward, and it fits cleanly into our automated data-ingestion pathways. Distributed Impala and Spark queries over terabytes of experimental rows are fast enough for our needs, which helps us extract data quickly even when several labs are running at the same time. Workload monitoring in the platform also detects malformed data frames and cluster partitions before any distributed training is carried out. The initial walkthroughs we received helped our practitioners get started smoothly.

What do you dislike about the product?

Fine-tuning resource queues in YARN and administering security policies across different analytical teams isn’t an easy task without a platform engineer. Connecting specific high-frequency data streams to the cluster also runs into handshaking issues, especially during peak hours when batch processing is busy. The recommendations around cluster performance tend to focus more on generic server sizing than on optimizing machine learning workloads. On top of that, the occasional runtime exceptions are routed through slow ticketing queues, and the enterprise licensing is not something to take lightly.

What problems is the product solving and how is that benefiting you?

Previously, our group struggled with disconnected storage servers, untracked feature matrices, and pipeline crashes from out-of-memory conditions when parallel analytical scripts competed for local processing power. With Cloudera, our distributed feature sets, model-training registries, and benchmarking analytics are now centralized through the clear interface of Cloudera Manager. This gives our researchers a straightforward way to monitor job runs and cluster node status without needing to use the command line.

Native integration also works smoothly with our Python environments and cloud data buckets, so we can feed high-throughput data directly into Apache Spark and Impala without running into file-locking issues. Query performance has been extremely fast and reliable, even with terabytes of experimental data and the concurrent demands of day-to-day laboratory work.

In addition, the platform’s intelligent workload engine can detect skewed partitions and abnormal data frames before training cycles begin. Thanks to guided onboarding and dedicated technical assistance, our group was able to scale the infrastructure successfully and achieve a reliable operational return on investment by skipping unnecessary preprocessing steps and maintaining reproducibility across experiments.

What do you like best about the product?

The software enables very fast data processing and is well optimized for analytics workloads. It offers solid storage capacity and integrates smoothly with many other services. I can store data from virtually any file source, and I really like the unstructured data filtering features. Overall, it delivers consistently fast performance.

What do you dislike about the product?

It has limited integration with certain services, and it takes time to become familiar with all aspects of the tool. Integrating legacy systems can be difficult, and at times it fails to connect to storage.

What problems is the product solving and how is that benefiting you?

We frequently use it to store raw data, and it helps us manage and store large volumes efficiently. It allows us to keep vast amounts of information and supports a wide range of storage capacities. We also use it to maintain the right data in the warehouse, and the product continues to deliver constant innovation.

What do you like best about the product?

I love the platform’s scalable processing for large datasets because it helps me manage workloads efficiently. It makes it easier to handle large volumes of data and offers very straightforward scaling capabilities. Overall, I really appreciate how simple it is to scale when needed.

What do you dislike about the product?

The configuration can be confusing at times, and I occasionally run into cluster-related issues. Application logs aren’t easy to access or view, which makes troubleshooting harder. Scaling also isn’t always immediate and can be delayed.

What problems is the product solving and how is that benefiting you?

It helps us save time during development and provides strong flexibility for data processing. It has enabled us to optimize our processes, and we rely on it to store large volumes of data. It also reduces infrastructure management efforts and helps us manage workloads efficiently.

What do you like best about the product?

I love its open data storage architecture, and the data preparation for AI is impressive. It gathers data from various sources into a single location, and I really appreciate how well it unifies data from multiple sources. It also provides access to source data for AI, and several aspects of the overall approach are particularly compelling.

What do you dislike about the product?

Optimizing performance can take some expertise, and troubleshooting may require a fair amount of effort. The interface can feel a bit complex at first, and I think the onboarding process would benefit from more guidance to help new users get started.

What problems is the product solving and how is that benefiting you?

Performance has improved, and it supports a wide range of analytical workloads. It helps me gather data from various sources and has made our workflow design much simpler. I’m able to analyze data more efficiently and spend less time on data preparation. It also helps us organize and process large volumes of data in a more structured way.

What do you like best about the product?

A hybrid data platform approach that enables organizations to manage, govern, and analyze data across on-premises systems, public clouds, and edge environments in a more unified and consistent way. Its main strength is the unified platform for data engineering, analytics, and AI.

What do you dislike about the product?

Cloudera could be complex and expensive, especially for organizations that do not need a full enterprise-scale data platform. Its steeper learning curve, and the need for skilled teams to manage it.

What problems is the product solving and how is that benefiting you?

To help handling large, messy, distributed data that is too expensive or too rigid for traditional data warehouses. It helps organizations store, process, govern, and analyze data across different systems while keeping batch, SQL, search, and real-time workloads in one platform.

What do you like best about the product?

The support team we get with Cloudera is amazing. I mean, they’re great people, and honestly they helped us out a bunch when we were knee-deep in trouble, so I really appreciate the support team.

What do you dislike about the product?

Cloudera upgrades can sometimes be complex because of dependencies between different services, parcels, Java versions, and configuration changes. Planning and validation are important to avoid impacting production workloads.

What problems is the product solving and how is that benefiting you?

Cloudera provides a centralized platform for managing and monitoring Hadoop clusters and services. It helps us manage components like HDFS, YARN, Hive, Kafka, Ranger, and ZooKeeper from a single interface, making cluster administration, monitoring, configuration management, and troubleshooting more efficient. It also helps us maintain cluster stability and identify issues before they significantly impact production workloads.

What do you like best about the product?

What I value most is having access to a stable, up-to-date big data stack that I can run on premises. I also like the flexibility to deploy the platform on premises, in a public cloud, or in a hybrid setup—whatever best fits the environment.

Cloudera data services, in particular, feels like a game changer. It enables users to quickly deploy warehouses, run data engineering jobs, build end-to-end machine learning pipelines, deploy ML models as API/web applications, and roll out LLMs as inference services for enterprise AI. On top of that, Cloudera support has been very helpful and generally responds quickly.

Cloudera is also easy to integrate with our customers existing stack, whether BI tools like Qlik and Tableau, or ETL like Talend and IBM DataStage. It's easy to setup connections from Hive/Impala to Talend for ETL purpose, and then connect them also to Qlik for dashboards.

What do you dislike about the product?

The UI can feel a bit clunky at times and doesn’t come across as very modern. Also, because it’s geared toward stability, integrating some cutting-edge tools with the Cloudera stack can be quite difficult. On top of that, a few of our customers have mentioned that the pricing feels on the higher side compared with competitors.

What problems is the product solving and how is that benefiting you?

We help implement Cloudera for data warehouse migration and modernization, big data initiatives, and Generative AI initiatives. For data warehouse modernization and big data initiatives, our customers benefit from a modern data lakehouse stack, improved performance, and reduced data silos by creating a single source of truth on top of Cloudera. With Cloudera data services, our customers can explore data faster and run ad-hoc reports more easily, powered by Cloudera Data Explorer and Impala on top of virtual warehouses. Cloudera AI also helps our customers get started with their Generative AI use cases, enabling them to quickly create LLM-powered apps without needing to manually set up infrastructure and security.

What do you like best about the product?

Great platform for hosting my tech ecosystem and AI solutions

What do you dislike about the product?

More capabilities on cloud since most of my data is on cloud

What problems is the product solving and how is that benefiting you?

Great platform to host my systems

What do you like best about the product?

one platform served anything essentials in data engineering

What do you dislike about the product?

interface less user friendly, long loading time

What problems is the product solving and how is that benefiting you?

big data processing