Google Cloud Platform (GCP) provides Cloud Bigtable, a fully managed NoSQL database service. It is intended to manage large amounts of structured and semi-structured data while maintaining high throughput and low latency. Many Google products, including Gmail, Google Search, and Google Analytics, use it.
If you’re thinking about “building an application that needs low latency and high throughput” and aren’t sure which database to employ that can scale for numerous reads and writes, Cloud Bigtable may be the ideal choice for many seeking solutions.
Overview of Bigtable
- Cloud Bigtable is a fully managed petabyte-scale wide-column NoSQL database.
- It is designed for low latency, massive amounts of reads and writes, and scaling performance.
- To simplify integration with the Apache ecosystem, Bigtable supports the open-source HBase API standard, which includes HBase, Beam, Hadoop, and Spark.
- It also works with the Google Cloud ecosystem and products such as Memorystore, BigQuery, Dataproc, Dataflow, and many others.
Cloud Bigtable: NoSQL Database Service
NoSQL, which stands for “not only SQL,” is a database designed to handle unstructured or semi-structured data, which can be challenging to manage in typical relational databases. In contrast to relational databases, which store data in tables with predetermined schemas, NoSQL databases, such as Cloud Bigtable, store data in a more flexible and scalable manner, frequently using key-value pairs or document-based storage.
Cloud Bigtable is especially well-suited for managing massive volumes of data that demand high read and write throughput. It is frequently used by businesses that must manage huge volumes of data, such as those in finance, telecommunications, and social media. Cloud Bigtable can manage nearly any amount of data with minimal latency and high availability due to its ability to scale horizontally by adding more nodes to a cluster.
- Scalability: Cloud Bigtable is designed to scale horizontally, so you can add more nodes to your cluster to increase capacity and performance.
- High Performance: It provides low-latency reads and writes for your data, making it ideal for applications that require fast access to data.
- Integrated with other GCP Services: Cloud Bigtable integrates with many other GCP services such as Cloud Dataflow, Cloud Dataproc, and Cloud Pub/Sub.
- Fully Managed: Cloud Bigtable is fully managed, meaning that Google takes care of the infrastructure, so you can focus on developing your applications.
- Data is by default encrypted with Google-managed encryption keys. But if there are any specific compliance and regulatory requirements and customers need to manage their keys, then CMEK is also supported.
- Bigtable backups let you save a copy of a table’s schema and data and then restore from the backup to a new table later.
- Offers Seamless scaling and replication
- Fast and performant
- High throughput at low latency
- Cluster resizing without downtime
CryptoCaster Quick Check:
Bigtable is suited for key/value data applications that require very high throughput and scalability, with each value often no greater than 10 MB. Bigtable also performs admirably as a storage engine for batch MapReduce operations, stream processing/analytics, and machine-learning applications.
Bigtablecan be used to store and query all of the following types of data:
- Time-series data, such as CPU and memory usage over time for multiple servers.
- Marketing data, such as purchase histories and customer preferences.
- Financial data, such as transaction histories, stock prices, and currency exchange rates.
- Internet of Things data, such as usage reports from energy meters and home appliances.
- Graph data, such as information about how users are connected.
- The client request is served by the front-end server pool,, which routes the read/write request to the BigTable cluster evenly,, which in turn reads/writes the data to a block of the contagious row,, also known as a tablet.
- Nodes are instance that belongs to the Bigtable cluster and handles a subset of the request if one node is doing a heavy task,, that’s called hot-spotting.
- More nodes can be added to increase the traffic load.
- Tablets are stored in Colossus (google file system) in SSTable format, which is persistent. BigTable nodes don’t store any data; instead, they only have pointers to each tablet that are stored on colossus.
- Apart from SSTables, writes are also written to shared log to increase durability.
Bigtable Storage Model
Bigtable is a distributed, sparse, multidimensional sorted map storage model. It is intended to manage huge amounts of structured and semi-structured data while maintaining low latency and high throughput. Data is structured into tables in the Bigtable storage paradigm, which are made up of rows and columns. A unique row key identifies each row in a table, and each column is distinguished by a column family and a column qualifier.
One of Bigtable’s primary features is its ability to manage large-scale data sets with high read and write performance needs. It is also designed to be fault-tolerant and highly available, with data replicated across a cluster’s many nodes. As a result, it is a good choice for use cases that require immediate access to enormous amounts of data, such as financial trading systems, social networking sites, and online gaming applications.
Bigtable stores data in massively scalable tables, each of which is a sorted key/value map.
- The table is made up of rows that describe a single entity and columns that contain the individual values for each row.
- Each row is indexed by a single row key, and columns that are linked to one another are grouped into a column family. Columns are recognized by a combination of the column family and a column qualifier (a unique name within the column family).
- Each row/column intersection can have many cells, each of which contains a unique timestamped version of the data for that row and column. Each cell in a particular row and column has its own timestamp, which is commonly indicated by (t).
- Storing numerous cells in a column offers a record of how the recorded data for that row and column has changed over time.
Use-Cases Of Cloud Bigtable
1.) Financial Analysis
Users and businesses can create models based on past data, continuously update fraud tendencies, and compare real-time transactions. They can also store and consolidate market data, trade operations, and other types of data such as social and transactional data.
High-performance databases that can manage massive volumes of data and enable real-time access to that data are required by financial institutions. Cloud Bigtable can be utilized for a variety of purposes, including fraud detection, risk analysis, and trading platforms.
Ingest and analyze large volumes of time series data from sensors in real-time, matching the high speeds of IoT data to track normal and abnormal behaviour. Enable customers to build dashboards and drive analytics on their data in real time.
Ad tech platforms often require the ability to store and retrieve vast amounts of data in real-time, including user profiles, ad impressions, and performance metrics. Cloud Bigtable can be used to provide the high-speed data processing and low-latency access that ad tech platforms require.
4.) Social media
Social media platforms generate vast amounts of data, including user profiles, posts, and interactions. Cloud Bigtable can handle the high write and read throughput requirements of these systems, while also providing low latency access to the data.
Online gaming platforms generate a large amount of data, including player data, game logs, and user-generated content. Cloud Bigtable can handle the high write and read throughput requirements of these systems, while also providing low latency access to the data.
Bigtable vs BigQuery
One of the most often asked questions during the live sessions is how Bigtable differs from BigQuery. So here’s the answer to your query.
Cloud Bigtable is a NoSQL database service that can handle huge amounts of structured and semi-structured data while maintaining fast performance and low latency. It is designed to handle real-time, high-speed data like time series and streaming data. It is a highly scalable, fully managed service that gives your data with low-latency reads and writes. Cloud Bigtable is suited for IoT applications, financial trading systems, and ad tech platforms that require high write and read performance.
Bigtable is a NoSQL wide-column database built for low latency, massive numbers of reads and writes, and scaling performance. If fast throughput and low latency at scale are not goals for you, another NoSQL database, such as Firestore, may be a better option.
BigQuery, on the other hand, is a fully-managed, serverless cloud data warehouse service built for analyzing massive amounts of structured and semi-structured data. It’s a highly scalable, low-cost service that lets customers store and analyze big datasets with SQL-like queries. BigQuery is designed to handle complicated, ad-hoc searches on massive datasets, and it can manage data from several sources. It is suited for ad hoc analysis and data exploration in use cases such as business intelligence, data warehousing, and data science. BigQuery is a large-scale enterprise data warehouse for structured relational data that is optimized for large-scale, ad-hoc SQL-based analysis and reporting, making it ideal for getting organizational insights.
Bigtable is the database of choice for IoT, AdTech, FinTech, gaming, and ML-based personalizations that require a specified level of scalability or throughput with stringent latency constraints. Through Cloud Pub/Sub, users can ingest hundreds of thousands of events per second from websites or IoT devices, process them in Dataflow, and transmit them to Cloud Bigtable.
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