Google BigTable

  • Author: Ronald Fung

  • Creation Date: 7 June 2023

  • Next Modified Date: 7 June 2024

A. Introduction

Google Bigtable is a distributed NoSQL database designed to store and process massive amounts of data. It is a fully managed service that provides high availability, scalability, and performance for large-scale applications.

Bigtable is built on Google’s proprietary distributed systems technology and is used by many Google services, such as Google Search, Google Maps, and Google Analytics.

Bigtable is a non-relational database, which means it does not use SQL for querying data. Instead, it uses a proprietary API that allows developers to access data stored in Bigtable. Bigtable is designed to handle structured and semi-structured data, such as web pages, images, and log files.

B. How is it used at Seagen

Google Bigtable is a distributed NoSQL database that is well-suited for storing and processing large amounts of structured and semi-structured data. Here are some ways Seagen can use Google Bigtable:

  1. Genomics data storage: Seagen can use Bigtable to store and process genomics data, including DNA sequencing data and related metadata. Bigtable’s scalability and performance make it ideal for handling large-scale genomics datasets.

  2. Clinical trial data management: Seagen can use Bigtable to store and manage clinical trial data, such as patient data, trial protocols, and outcomes data. Bigtable’s strong consistency guarantees and automatic sharding can help ensure data accuracy and availability.

  3. Real-time data processing: Seagen can use Bigtable to process real-time data streams, such as sensor data from medical devices or social media data. Bigtable’s high throughput and low latency make it well-suited for real-time data processing.

  4. Data analytics: Seagen can use Bigtable in conjunction with other Google Cloud Platform services, such as BigQuery and Dataflow, to perform advanced analytics on their data. Bigtable can be used to store and process large datasets, while BigQuery can be used to perform complex queries and Dataflow can be used to process data pipelines.

Overall, Google Bigtable can help Seagen store and process large-scale data, including genomics data, clinical trial data, and real-time data streams. By using Bigtable in conjunction with other Google Cloud Platform services, Seagen can perform advanced analytics on their data and gain insights that can inform their research and development efforts.

C. Features

High throughput and low latency at any scale

Bigtable is a key-value and wide-column store, ideal for fast access to very large amounts of structured, semi-structured, or unstructured data with high read and write throughput. Bigtable powers many core Google services such as YouTube, Google Analytics, Search, Ads, Drive, and Maps.

Cluster resizing without downtime

Scale seamlessly from thousands to millions of reads/writes per second. Bigtable throughput can be dynamically adjusted by adding or removing cluster nodes—all without any downtime. Bigtable can also autoscale your cluster based on changes in demand so that you can maintain great performance in the most cost-effective way.

Flexible, automated replication to optimize any workload

Write data once and automatically replicate where needed with eventual consistency—giving you control for high availability and isolation of read and write workloads. No manual steps needed to ensure consistency, repair data, or synchronize writes and deletes. Benefit from a high availability SLA of 99.999% for instances with multi-cluster routing across 3 or more regions (99.9% for single-cluster instances).

Easy migrations from Apache HBase and Cassandra to Bigtable

Live migrations enable faster and simpler migrations from HBase to Bigtable by ensuring accurate data migration, reducing migration effort, and providing a better overall developer experience. HBase Bigtable Replication Library allows for no-downtime live migrations, Import Tool easily loads HBase snapshots into Bigtable, and Validation Tool ensures accurate data migration. Dataflow templates simplify migrations from Cassandra to Bigtable.

Enterprise-grade security and controls

Customer-managed encryption keys (CMEK) with External Key Manager support, IAM integration for access and controls, support for VPC-SC, and comprehensive audit logging help ensure your data is protected and complies with regulations.

D. Where Implemented

LeanIX

E. How it is tested

Testing Google Bigtable involves ensuring that the data is being stored and retrieved correctly, and that the system is functioning as expected. Here are some ways to test Google Bigtable:

  1. Create test data: Create a small set of test data that represents the data you will be storing in Bigtable. This will allow you to test the system’s ability to store and retrieve data.

  2. Use the emulator: Google provides an emulator for Bigtable that allows you to test your applications locally. This allows you to test your application’s interactions with Bigtable without incurring any costs.

  3. Use the Bigtable shell: The Bigtable shell is a command-line tool that allows you to interact with Bigtable and test its functionality. You can use the shell to create tables, add data, and perform queries.

  4. Use the Bigtable client libraries: Google provides client libraries for a number of programming languages, including Java, Python, and Go. You can use these libraries to test your application’s interactions with Bigtable.

  5. Monitor performance: Use Bigtable’s monitoring tools to monitor the system’s performance and ensure that it is functioning as expected. You can use tools like Stackdriver to monitor performance metrics and set up alerts for issues.

Overall, testing Google Bigtable involves creating test data, using the emulator, using the Bigtable shell, using the Bigtable client libraries, and monitoring performance. By testing the system thoroughly, you can ensure that it is functioning as expected and that your applications are interacting with Bigtable correctly.

F. 2023 Roadmap

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G. 2024 Roadmap

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H. Known Issues

Google Bigtable is a powerful NoSQL database that is designed to handle large-scale data sets and high-throughput workloads. While Bigtable is a robust and reliable system, there are some known issues that users should be aware of. Here are some of the most common known issues for Google Bigtable:

  1. Limited query support: Bigtable does not support SQL queries, which can make it difficult to perform complex queries. Users must use Bigtable’s proprietary API to query data, which can be more complex than using SQL.

  2. Cost: Bigtable is a pay-per-usage service, and costs can quickly add up for users with large data sets or high-throughput workloads. Users should carefully monitor their usage and consider using cost-saving measures, such as data compression and efficient query design.

  3. Data consistency: Bigtable provides strong consistency guarantees for data stored in the database, but this can impact performance and throughput. Users should be aware of the trade-offs between consistency and performance when designing their applications.

  4. Limited indexing: Bigtable does not support secondary indexes, which can make it difficult to perform efficient queries on specific columns. Users must design their data models carefully to ensure efficient querying.

  5. Limited tooling: While Bigtable provides a number of monitoring and management tools, there are limited third-party tools available for working with Bigtable. This can make it difficult for users to manage and monitor their Bigtable instances.

Overall, Google Bigtable is a powerful NoSQL database that is designed to handle large-scale data sets and high-throughput workloads. Users should be aware of these known issues and take steps to optimize their applications to ensure efficient querying, minimize costs, and manage data consistency.

[x] Reviewed by Enterprise Architecture

[x] Reviewed by Application Development

[x] Reviewed by Data Architecture