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's not structured support, which is why we don't use purely open-source projects without additional structured support.
I have been self-taught and I have been able to handle all my problems alone.
rating the customer support at ten points out of ten
In the past one and a half years that I have been running with BigQuery, I have not needed to raise any technical support with BigQuery or with Google.
It is a distributed file system and scales reasonably well as long as it is given sufficient resources.
It is a 10 out of 10 in terms of scalability.
We have not seen problems with scaling.
The scalability is definitely good because we are migrating to the cloud since the computers on the premises or the big database we need are no longer enough.
Continuous management in the way of upgrades and technical management is necessary to ensure that it remains effective.
In the past one and a half years that I have been running with BigQuery, I have not needed to raise any technical support with BigQuery or with Google.
The problem with Apache Hadoop arose when the guys that originally set it up left the firm, and the group that later owned it didn't have enough technical resources to properly maintain it.
BigQuery is already integrating Gemini AI into the data extraction process directly in order to reduce costs.
Troubleshooting requires opening each pipeline individually, which is time-consuming.
In general, if I know SQL and start playing around, it will start making sense.
Being able to optimize the queries to data is critical. Otherwise, you could spend a fortune.
The price is perceived as expensive, rated at eight out of ten in terms of costliness.
Hadoop is a distributed file system, and it scales reasonably well provided you give it sufficient resources.
I assess Apache Hadoop's fault tolerance during hardware failures positively since we have hardware failover, which works without problems.
It is really fast because it can process millions of rows in just a matter of one or two seconds.
BigQuery processes a substantial amount of data, whether in gigabytes or terabytes, swiftly producing desired data within one or two minutes.
Google platform in general has been quite stable, and we do not see that many outages at resource level or at overall zonal level.
| Company Size | Count |
|---|---|
| Small Business | 14 |
| Midsize Enterprise | 8 |
| Large Enterprise | 22 |
| Company Size | Count |
|---|---|
| Small Business | 13 |
| Midsize Enterprise | 10 |
| Large Enterprise | 20 |
Apache Hadoop provides a scalable, cost-effective open-source platform capable of handling vast data volumes with features like HDFS, distributed processing, and high integration capabilities.
Apache Hadoop is known for its distributed file system HDFS, which supports large data volumes efficiently. Its open-source nature allows cost-effective scalability and compatibility with tools like Spark for enhanced analytics. While it offers significant processing power, areas for improvement include user-friendliness, interface design, security measures, and real-time data handling. Users benefit from data storage for structured and unstructured data, facilitated by its distributed processing architecture. Data replication ensures fault tolerance, while its capability to integrate with tools like Apache Atlas and Talend highlights its versatility.
What are the key features of Apache Hadoop?Industries leverage Apache Hadoop for Big Data analytics, data lakes, ETL tasks, and enterprise data hubs, handling unstructured and structured data from IoT, RDBMS, and real-time streams. Its applications extend to data warehousing, AI/ML projects, and data migration, employing tools like Apache Ranger, Hive, and Talend for effective data management and analysis.
BigQuery is a powerful cloud-based data warehouse offering advanced SQL querying, seamless Google integration, and scalable handling of large datasets. Its serverless architecture and built-in AI capabilities facilitate efficient data processing and insights extraction.
BigQuery provides an efficient data analysis platform with low-latency performance and cost-effective on-demand pricing. Leveraging Google's cloud infrastructure for data storage, it offers robust security and high availability. While it excels in SQL support and caching features, it can improve on user accessibility, integration with diverse tools, and machine learning feature expansion. Making it more accessible for smaller entities through improved cost management and local data compliance is essential. Enhancements in query speed and intuitive interfaces can further optimize performance.
What features are offered by BigQuery?In industries like healthcare, finance, and marketing, BigQuery is extensively used for data storage, generating reports, and supporting ETL processes. Educational institutions leverage it for analytics, aligning seamlessly with Google Cloud for serverless infrastructure efficiencies.
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