Google’s Bigtable vs BigQuery: Key Differences Simplified 101

BigQuery and Bigtable are the two most powerful tools in Google Cloud for handling large datasets with ease. While both handle large volumes of data, They serve different purposes. Bigtable is a NoSQL database used for low latency and high throughput operations, which makes it ideal for IoT (Internet of Things) data. Whereas BigQuery is a fully managed data warehouse for big data analytics.

In this article, you’ll be taken through critical aspects of Bigtable vs BigQuery. But beforehand, let’s briefly discuss the two platforms.

Introduction to BigQuery

Google’s BigQuery is a powerful Business Intelligence (BI) platform that works as a “Big Data as a Service” solution. It is a tool that creates real-time analytic reports on Big Data to help you generate useful insights to make effective business decisions.BigQuery can manage all data types across clouds, structured or unstructured, with granular access controls. BigQuery also provides BigQuery ML, which allows you to create and run ML models on your BigQuery data.

Introduction to Bigtable

Google’s Bigtable, often known as a “NoSQL Database as a Service,” is a Petabyte-scale, fully managed NoSQL database service. Bigtable can index, query, and analyze enormous volumes of data and supports weak consistency. Bigtable is a key-value and wide-column store, which makes it ideal for fast access to structured, semi-structured, and unstructured data. Bigtable separates computing resources from data storage resources, which makes it possible to adjust processing resources transparently.

Google’s Bigtable vs BigQuery: A Comprehensive Comparison

Bigtable vs BigQuery

BigQuery	Bigtable
BigQuery is an OLAP (Online Analytical Processing) solution. As query latency is high, BigQuery is best suited for queries with high workloads, such as standard OLAP reporting and archiving activities. BigQuery’s architecture discourages OLTP-style queries. To put this in perspective, small read-write operations in BigQuery take about 1.8 seconds, while the identical action in Bigtable takes only 9 Milliseconds.	Bigtable is ideal for OLTP workloads because of its quick read-by-key and update operations. Tables store information in the data model, and rows have columns (Type Array or Struct). A column can be added to a row; the structure is similar to that of a persistent map. Rows feature the main key that is unique to each entry, facilitating simple reading and updating. As Bigtable has efficient support for key-range-iteration, it is possible to run reporting/OLAP workloads in it. Use BigQuery alternatively if interactive querying in an Online Analytical Processing environment is a top priority.
BigQuery allows you to conduct complicated analytical SQL-Based queries against big datasets. Users can, however, make use of NoSQL approaches. For performance reasons, it is recommended to denormalize data while developing schemas and importing data into BigQuery.	Bigtable is a NoSQL Database service that does not allow SQL or multi-row transactions. It is not a Relational Database. Because of this, Bigtable is inappropriate for a wide range of applications; it is only suitable for changeable data sets with a minimum data size of 1 Terabyte—anything smaller results in excessive overhead.
BigQuery is an excellent solution for queries that require a “Table Scan” or a search throughout the entire database (e.g. calculating Sums, Averages, Counts, or Groupings). BigQuery, on the other hand, also has a lot of limits, including a daily limit on table updates and a limit on data size per request. This is because BigQuery isn’t meant to be a replacement for typical Relational Databases; instead, it’s designed to conduct analytical queries rather than basic CRUD activities.	Bigtable‘s main features are its ability to grow horizontally (resulting in extremely high read/write throughput) and its “key-columns”—i.e., one key can have numerous columns that can be modified. When storing individual data elements larger than 10 Gigabytes, performance diminishes. Cloud storage is most likely a better alternative if you need a complete storage solution for unstructured things (e.g. video files). Bigtable works best with Time-Series data (for example, CPU and Memory utilization over time for several servers), Financial data (for example, Transaction Histories, Stock Prices, and Currency Exchange Rates), and IoT (Internet of Things) use cases.
BigQuery data is immutable: once uploaded, an object cannot be changed during its storage lifespan, and data cannot be deleted or altered for a set period of time. If an existing record needs to be modified, the partition must be rewritten. BigQuery is an “append-only” database that reduces the amount of data saved by automatically removing partitions that are older than the pre-configured time to live.	Bigtable organizes data into scalable tables, each of which is a sorted key/value map with a column key, row key, and timestamp as indexes. This allows for changeable data as well as quick key-based lookups. Each column has separate values for each row, and each row normally defines a single object. Regardless of how many columns are read or written within a row, read and write operations on data to rows are atomic.

Conclusion

In summary, Bigtable and BigQuery each cater to different data needs. Bigtable is a mutable NoSQL database for OLTP use cases, perfect for real-time applications like IoT. BigQuery, on the other hand, is an immutable, SQL-based data warehouse built for OLAP tasks, such as analytics and business intelligence.

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