We performed a comparison between Apache Hadoop and BigQuery 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."It is a file system for data collection. There are nodes in this cluster that contain all the information, directories, and other files. The nodes are based on the MySQL database."
"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."
"Hadoop is designed to be scalable, so I don't think that it has limitations in regards to scalability."
"Data ingestion: It has rapid speed, if Apache Accumulo is used."
"Since both Apache Hadoop and Amazon EC2 are elastic in nature, we can scale and expand on demand for a specific PoC, and scale down when it's done."
"It's good for storing historical data and handling analytics on a huge amount of data."
"The scalability of Apache Hadoop is very good."
"Hadoop is extensible — it's elastic."
"There are some performance features like partitioning, which you can do based on an integer, and it improves the performance a lot."
"The product is serverless. We only need to write SQL queries to analyze the data. We need to pay based on the number of queries. The retrieval time is very less. Even if you write large queries, the tool is able to bring back data in a few seconds."
"Even non-coders can review the data in BigQuery."
"The initial setup is straightforward."
"BigQuery can be used for any type of company. It has the capability of building applications and storing data. It can be used for OLTP or OLAP. It has many other products within the Google space."
"The query tool is scalable and allows for petabytes of data."
"The setup is simple."
"As a cloud solution, it's easy to set up."
"Based on our needs, we would like to see a tool for data visualization and enhanced Ambari for management, plus a pre-built IoT hub/model. These would reduce our efforts and the time needed to prove to a customer that this will help them."
"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is probably the only feature we can improve a little bit because the terminal and coding screen on Hadoop is a little outdated, and it looks like the old C++ bio screen.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 load optimization capabilities of the product are an area of concern where improvements are required."
"The solution is very expensive."
"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."
"General installation/dependency issues were there, but were not a major, complex issue. While migrating data from MySQL to Hive, things are a little challenging, but we were able to get through that with support from forums and a little trial and error."
"We would like to have more dynamics in merging this machine data with other internal data to make more meaning out of it."
"It would be good to have more advanced analytics tools."
"For greater flexibility and ease of use, it would be beneficial if BigQuery offered more third-party add-ons and connectors, particularly for databases that don't have built-in integration options."
"We would like to be able to calibrate the solution to run on top of a raw file."
"I would like to see version-based implementation and a fallback arrangement for data stored in BigQuery storage. These are some features I'm interested in."
"The processing capability can be an area of improvement."
"The main challenges are in the areas of performance and cost optimizations."
"As a product, BigQuery still requires a lot of maturity to accommodate other use cases and to be widely acceptable across other organizations."
"There are many tools that you have to use with BigQuery that are different services also provided for by Google. They need to all be integrated into BigQuery to make the solution easier to use."
"There are some limitations in the query latency compared to what it was three years ago."
Apache Hadoop is ranked 5th in Data Warehouse with 33 reviews while BigQuery is ranked 5th in Cloud Data Warehouse with 31 reviews. Apache Hadoop is rated 7.8, while BigQuery is rated 8.2. 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 BigQuery writes "Expandable and easy to set up but needs more local data residency". Apache Hadoop is most compared with Azure Data Factory, Microsoft Azure Synapse Analytics, Oracle Exadata, Snowflake and VMware Tanzu Greenplum, whereas BigQuery is most compared with Snowflake, Teradata, Oracle Autonomous Data Warehouse, Vertica and AWS Lake Formation. See our Apache Hadoop vs. BigQuery report.
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