Google Cloud offers a robust streaming analytics ecosystem to process continuous data streams from diverse sources. BigQuery is an ideal ingestion source for known schemas, with BigQuery's storage write API and continuous queries enabling direct data ingestion, maximizing data freshness within the warehouse and allowing you to connect to other sources. Bigtable, on the other hand, offers global linear scalability and built-in data synchronization, excelling at flexible and dynamic schemas that need immediate read-after-write consistency. Bigtable also provides out of the box timestamp based versioning and automated time to live (TTL) retention policies, making it an ideal storage and analysis choice for streaming events. The amount of data you need to stream is also a consideration. BigQuery can stream data in single GBps per second in the US and EU multi-regions with hundreds of MBps per second in other regions. Bigtable provides more flexibility in capturing streaming data, with linear scalability of 14,000 writes per second per node in any region supported by Bigtable. 

With the combination of BigQuery and Bigtable, you don’t need to make tradeoffs but can choose the right ingestion technique for the use case.

With most databases, you must choose between fast row retrieval or large-scale analytics processing. Bigtable and BigQuery combine their distinct purposes to provide a complete real-time analytics database, regardless of query type. 

Bigtable shines as a storage engine optimized for lightning-fast retrieval of single rows or ranges of data, making it ideal for user-facing applications that need real-time responsiveness such as charts within an application, profile lookups, time-series analysis, metrics on streaming data such as clicks, or any other query that is predictable and needs to be served at high volume and with low latency. Bigtable is based on a log structured engine (LSM tree) which optimizes for high performance using a combination of in-memory and disaggregated disk storage along with specialized client libraries that offer both synchronous and asynchronous access. Bigtable's flexible schema, and self-managing capabilities further enhance its suitability for demanding applications. In contrast, BigQuery excels at analytics workloads, providing powerful tools for querying and analyzing large datasets with complex aggregations, integrations with Vertex AI, and transformations.

By choosing the combination of Bigtable and BigQuery you don’t need to choose between individual row lookups and comprehensive analytical processing over massive datasets. The seamless integration makes it easy to use either storage model to power your real-time application. Bigtable is often used as a cost-effective caching solution for BigQuery sized datasets. For example, BigQuery can be used for generating embeddings in batch and then serving those embeddings in Bigtable to support Retrieval Augmented Generation (RAG) applications.

Bigtable offers specialized data types that pre-process your data as it's written, giving you instant results and insights. You can calculate sums, minimums, maximums, and approximate distinct counts as data is written, with built-in global replication for consistent results across your entire application. These data types are also fully interoperable with warehouse data that can be loaded from BigQuery.

Real-time data aggregation can power the creation of comprehensive machine learning features, leading to accurate predictions that can engage users in the moment.

Bigtable is integrated into the BigQuery analytics ecosystem, making your real time data easily accessible for additional stream analysis. You can query and join Bigtable data with other datasets in BigQuery with external tables, use open source Spark analytics or Apache Beam pipelines directly on Bigtable data, and write back results. This analytics access can also use Bigtable's Data Boost to get high-performance analytics without affecting your real-time application performance. Plus, with Change Data Capture (CDC) and Dataplex, exporting and discovering your Bigtable data is simple and does not require complex or customized data synchronization tasks.