Bigtable for time-series data in GCP - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is Google Cloud Bigtable primarily used for?

Google Cloud Bigtable is a fast, fully managed NoSQL database service designed for large-scale, low-latency workloads such as time-series data, IoT data, and real-time analytics.

beginner

Why is Bigtable a good choice for time-series data?

Bigtable handles large volumes of sequential data efficiently, supports high write throughput, and allows fast range queries, which are common in time-series data analysis.

intermediate

How should row keys be designed in Bigtable for time-series data?

Row keys should include a reversed timestamp or a hashed prefix to avoid hotspots and distribute writes evenly across nodes, improving performance and scalability.

intermediate

What is a common schema design pattern for storing time-series data in Bigtable?

Use a single table with row keys combining device ID and timestamp, and columns representing different metrics or attributes collected at each time point.

beginner

How does Bigtable handle scaling for time-series workloads?

Bigtable automatically splits and distributes data across nodes as the dataset grows, allowing seamless horizontal scaling to handle increasing time-series data volumes.

What type of database is Google Cloud Bigtable?

A NoSQL wide-column store

B Relational SQL database

C Graph database

D Document store

Bigtable is a NoSQL wide-column database designed for large-scale, low-latency workloads.

Which of the following is a best practice for row key design in Bigtable for time-series data?

A Use reversed timestamps or hashed prefixes

B Use sequential timestamps as row keys

C Use random UUIDs only

D Use device ID only

Reversed timestamps or hashed prefixes help distribute writes evenly and avoid hotspots.

What is a common use case for Bigtable?

A Storing small relational datasets

B Running complex SQL joins

C Storing large volumes of time-series data

D Hosting static websites

Bigtable is optimized for large-scale time-series and analytical data.

How does Bigtable scale as data grows?

A Manually add more nodes and repartition data

B Does not support scaling

C Requires downtime to scale

D Automatically splits and distributes data across nodes

Bigtable automatically manages data distribution and scaling without downtime.

In a time-series schema, what do columns typically represent in Bigtable?

A User credentials

B Different metrics or attributes collected at each timestamp

C Unique row identifiers

D Database indexes

Columns store different metrics or attributes for each time point in time-series data.

Explain why Bigtable is suitable for storing and querying time-series data.

Think about how time-series data grows and how queries often look for ranges of time.

Describe best practices for designing row keys and schema in Bigtable for time-series workloads.

Focus on how to organize data to keep performance high and avoid bottlenecks.