Need advice about which tool to choose?Ask the StackShare community!
Google BigQuery vs Xplenty: What are the differences?
What is Google BigQuery? Analyze terabytes of data in seconds. Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python..
What is Xplenty? Code-free data integration, data transformation and ETL in the cloud. Read and process data from cloud storage sources such as Amazon S3, Rackspace Cloud Files and IBM SoftLayer Object Storage. Once done processing, Xplenty allows you to connect with Amazon Redshift, SAP HANA and Google BigQuery. You can also store processed data back in your favorite relational database, cloud storage or key-value store.
Google BigQuery and Xplenty can be primarily classified as "Big Data as a Service" tools.
Some of the features offered by Google BigQuery are:
- All behind the scenes- Your queries can execute asynchronously in the background, and can be polled for status.
- Import data with ease- Bulk load your data using Google Cloud Storage or stream it in bursts of up to 1,000 rows per second.
- Affordable big data- The first Terabyte of data processed each month is free.
On the other hand, Xplenty provides the following key features:
- Xplenty provides you with an visual, intuitive interface to design your ETL data flows
- Xplenty lets you integrate data from a variety of data stores, such as Amazon RDS, MySQL, PostgreSQL, Microsoft SQL Server and MongoDB.
- Read and process data from cloud storage sources such as Amazon S3, Rackspace Cloud Files and IBM SoftLayer Object Storage
Cloud Data-warehouse is the centerpiece of modern Data platform. The choice of the most suitable solution is therefore fundamental.
Our benchmark was conducted over BigQuery and Snowflake. These solutions seem to match our goals but they have very different approaches.
BigQuery is notably the only 100% serverless cloud data-warehouse, which requires absolutely NO maintenance: no re-clustering, no compression, no index optimization, no storage management, no performance management. Snowflake requires to set up (paid) reclustering processes, to manage the performance allocated to each profile, etc. We can also mention Redshift, which we have eliminated because this technology requires even more ops operation.
BigQuery can therefore be set up with almost zero cost of human resources. Its on-demand pricing is particularly adapted to small workloads. 0 cost when the solution is not used, only pay for the query you're running. But quickly the use of slots (with monthly or per-minute commitment) will drastically reduce the cost of use. We've reduced by 10 the cost of our nightly batches by using flex slots.
Finally, a major advantage of BigQuery is its almost perfect integration with Google Cloud Platform services: Cloud functions, Dataflow, Data Studio, etc.
BigQuery is still evolving very quickly. The next milestone, BigQuery Omni, will allow to run queries over data stored in an external Cloud platform (Amazon S3 for example). It will be a major breakthrough in the history of cloud data-warehouses. Omni will compensate a weakness of BigQuery: transferring data in near real time from S3 to BQ is not easy today. It was even simpler to implement via Snowflake's Snowpipe solution.
We also plan to use the Machine Learning features built into BigQuery to accelerate our deployment of Data-Science-based projects. An opportunity only offered by the BigQuery solution