Google cloud provides various products for data solutions. Different products are designed for different scenarios. It's critical to understand the differences between products and choose the right product for your solutions. A commonly asked question for new starters is the differences between BigQuery and Cloud BigTable.
BigQuery is the data warehouse on Google Cloud for business analytics and insights. Cloud BigTable is one of the cloud native NoSQL database supporting large scale and low-latency workloads. These two products are very different.
Instead of talking about the key features of each product, we will go through the common use cases and the right product for each of them.
The following table summarizes some of the common scenarios and the suitable product for implementing them.
*bq represents BigQuery and bt represents Cloud BigTable in the following table.
Massive key-value data store and query with low latency
|bt||BigTable supports sub-10ms latency with high concurrency. It implements a key-value model. Google Search is built using BigTable.|
|Migrate on-premise HBase solutions to Cloud.||bt||BigTable is similar as HBase which are both key-value models.|
|Migrate SQL based data warehousing environment to Cloud.||bq||BigQuery supports SQL-like querying languages and are ideal for migrating on-premise SQL data warehousing solutions to Cloud.|
|Perform built-in ML engine and GIS supports.||bq||BigQuery has built-in ML engine (BigQuery ML) and BigQuery GIS to create models using SQL and to support geo-spatial related analytics.|
|Build a data lake solution||bq||BigQuery can be used to ingest and store massive data with different formats like JSON, CSV, parquet, Avro, etc.|
|Use Built-in BI engine to easily visualize data.||bq||BigQuery BI Engine integrates with BI tools like Google Data Studio, Tableau, Power BI and Looker to perform fast in-memory analysis.|
|Use Python and other Cloud SDK tools to ingest data||bq and bt||Both BigQuery and Cloud BigTable provides APIs for different programming languages.|
|Perform interactive OLAP workloads||bq|
|Build streaming analytics solution||bq||BigQuery supports ingesting and analyzing millions of rows of data and creating real-time dashboards.|
|Support petabyte-scale workloads||bt and bq||Both products support petabyte-scale workloads.|
Hopefully you now have a basic understanding of the different use case scenarios for BigQuery and Cloud BigTable. One thing to notice is that BigQuery also supports reading data stored in Cloud BigTable directly.