We performed a comparison between Apache Hadoop and AWS Lake Formation based on real PeerSpot user reviews.
Find out in this report how the two Cloud Data Warehouse solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."As compared to Hive on MapReduce, Impala on MPP returns results of SQL queries in a fairly short amount of time, and is relatively fast when reading data into other platforms like R."
"One valuable feature is that we can download data."
"Hadoop is extensible — it's elastic."
"The solution is easy to expand. We haven't seen any issues with it in that sense. We've added 10 servers, and we've added two nodes. We've been expanding since we started using it since we started out so small. Companies that need to scale shouldn't have a problem doing so."
"Most valuable features are HDFS and Kafka: Ingestion of huge volumes and variety of unstructured/semi-structured data is feasible, and it helps us to quickly onboard a new Big Data analytics prospect."
"The tool's stability is good."
"Hadoop is designed to be scalable, so I don't think that it has limitations in regards to scalability."
"The most valuable features are the ability to process the machine data at a high speed, and to add structure to our data so that we can generate relevant analytics."
"We use AWS Lake Formation typically for the data warehouse."
"The solution is quite good at handling analytics. It's done a good job at helping us centralize them."
"The solution has many features that are applicable to events such as audits."
"It is seamlessly integrated within the AWS ecosystem, making it straightforward to manage access patterns for AWS-native services."
"The most important advantage in using AWS Lake Formation is its ability to connect the data lake to the other technologies in AWS. This is what I advise my clients."
"I would like to see more direct integration of visualization applications."
"Real-time data processing is weak. This solution is very difficult to run and implement."
"I mentioned it definitely, and this is probably the only feature we can improve a little bit because the terminal and coding screen on Hadoop is a little outdated, and it looks like the old C++ bio screen. If the UI and UX can be improved slightly, I believe it will go a long way toward increasing adoption and effectiveness."
"The solution needs a better tutorial. There are only documents available currently. There's a lot of YouTube videos available. However, in terms of learning, we didn't have great success trying to learn that way. There needs to be better self-paced learning."
"It would be helpful to have more information on how to best apply this solution to smaller organizations, with less data, and grow the data lake."
"The key shortcoming is its inability to handle queries when there is insufficient memory. This limitation can be bypassed by processing the data in chunks."
"The solution is very expensive."
"What could be improved in Apache Hadoop is its user-friendliness. It's not that user-friendly, but maybe it's because I'm new to it. Sometimes it feels so tough to use, but it could be because of two aspects: one is my incompetency, for example, I don't know about all the features of Apache Hadoop, or maybe it's because of the limitations of the platform. For example, my team is maintaining the business glossary in Apache Atlas, but if you want to change any settings at the GUI level, an advanced level of coding or programming needs to be done in the back end, so it's not user-friendly."
"AWS Lake Formation's pricing could be cheaper."
"For the end-users, it's not as user-friendly as it could be."
"The solution could make improvements around orchestration and doing some automation stuff on AWS front automation. It would be useful if we could use automation to build images and use hardened images which are CIS compliant."
"In our experience what could be improved are not the support, performance or monitoring, but at a managerial level, the very expensive professional services of AWS. This could be an area of improvement for them. It's too expensive to acquire their support."
"It falls short when it comes to more granular access control, such as cell-level or row-level entitlements which is a significant drawback for organizations that require precise control over who can access specific rows of data."
Apache Hadoop is ranked 5th in Data Warehouse with 33 reviews while AWS Lake Formation is ranked 12th in Cloud Data Warehouse with 5 reviews. Apache Hadoop is rated 7.8, while AWS Lake Formation is rated 7.6. The top reviewer of Apache Hadoop writes "Handles huge data volumes and create your own workflows and tables but you need to have deeper knowledge". On the other hand, the top reviewer of AWS Lake Formation writes "Strategically aligning data management in a multi-cloud environment with significant reporting challenges". Apache Hadoop is most compared with Azure Data Factory, Microsoft Azure Synapse Analytics, Oracle Exadata, Snowflake and SAP BW4HANA, whereas AWS Lake Formation is most compared with Snowflake, Azure Data Factory, Amazon Redshift and Microsoft Azure Synapse Analytics. See our AWS Lake Formation vs. Apache Hadoop report.
See our list of best Cloud Data Warehouse vendors.
We monitor all Cloud Data Warehouse reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
