Google Cloud Bigtable vs. Google Cloud BigQuery – What’s the difference?

Many organizations are unsure whether to use BigQuery or Bigtable. These two services have many similarities, including the use of the term “large” within their titles. They can be used in a variety of ways in your big data ecosystem. Bigtable is a NoSQL large-column NoSQL database. It is optimized for low latency, large write and read volumes, and scalability. This NoSQL database has a large number columns that can handle high-volume reads or writes.
BigQuery can be viewed as a large-scale structured relational data warehouse. It is great for large-scale, ad hoc SQL-based research and reports, so it’s ideal for gaining organizational insight. You can also use Cloud Bigtable data.
To help you choose the right one, we will be covering all areas of Bigtbale & BigQuery in this blog.
What is Google Cloud Bigtable and how can it help you?
Cloud Bigtable is a sparsely populated table that can hold terabytes to even petabytes worth of information. It has billions of rows and thousands upon thousands of columns. Each row has one indexed value, also known as the row key. Bigtable excels at storing large amounts single-keyed data with low latency. It is also a great MapReduce data source because it has high read and write throughput with low latency.
The Bigtable can also store and query these types of data:
Firstly, Time-series data. This includes CPU and memory usage for multiple servers.
Secondly, Marketing data. This includes customer preferences and purchase history.
Thirdly, Financial data. This includes transaction history, stock prices, as well as currency exchange rates.
The Internet of Things data is next. This includes usage reports from energy meters as well as home appliances.
Graph data is the final category. This includes information about how users connect to one another.
Benefits of Bigtable
Cloud Bigtable is the storage engine for low-latency applications, high-throughput data processing, and analytics. It scales with you from your first gigabyte up to your petabyte-scale.
Second, start with one node per cluster and grow to hundreds of nodes as you need them to meet peak demand. Replication is a great option for live-serving apps. It provides high availability and workload segregation.
This fully managed service also connects to big data platforms like Hadoop, Dataflow and Dataproc easily. Developer teams will also find it easy to use the support for the open source HBase API standard.
What is Google Cloud BigQuery?
BigQuery is a fully-managed corporate data warehouse that includes built-in features to help manage and analyze your data. These include machine learning and geospatial analysis as well as business intelligence. BigQuery’s serverless architecture allows you to use SQL queries to solve the most important issues in your organization without requiring infrastructure management. BigQuery’s distributed, scalable analytical engine allows you to query petabytes and terabytes in seconds, while simultaneously consuming minutes for queries on petabytes.
BigQuery allows you to be more flexible by seperating the computational engine that analyzes your data from other storage options. BigQuery can also be used to store and analyze data or to evaluate data from other sources. BigQuery ML (and BI Engine) are powerful tools for understanding and analyzing data.
Two BigQuery interfaces are the Google Cloud Console or BigQuery’s command-line tool. Data scientists and developers can use client libraries in popular programming languages, including Java, JavaScript and Go, as well BigQuery’s REST API or RPC API to modify and manage data.
BigQuery Benefits
Cloud BigQuery allows you to get real-time updates about all your business operations through streaming data queries. Machine learning is built-in so you can predict the business consequences without needing to shift data.
In just a few clicks you can securely access and share your business’s analytical insights. Popular business intelligence tools allow you to create stunning reports and dashboards straight out of the box.
Finally, BigQuery’s security and governance measures ensure high availability and a 99.99 percent uptime SLA. Encryption is enabled default and keys are handled by clients.
We have now covered the basics of BigQuery and Cloud Bigtable. We will now learn more about the features of these services in the next section.
Google Cloud Bigtable Features
1. Low latency and high throughput
Bigtable is ideal for storing large amounts of data in key-value stores. It provides high read and writing throughput with minimal latency and allows for fast access to huge amounts of data. The throughput increases linearly so adding more Bigtable nodes to the network will increase the QPS (queries per minute). This is however based on the same infrastructure as Google’s billion-user products such as Search and Maps.
2. Cluster resizing without any downtime
Bigtable throughput can dynamically change by adding or deleting cluster members without restarting. This means that you can scale up a Bigtable cluster in a matter of hours to handle a large load and then scale back without downtime.
3. Automated replication to optimize any workload
Once you write data, it will automatically replicate where it is needed. This ensures high availability and separation between read and writing workloads. No human intervention is required to ensure consistency, correct data, synchronization writes and deletes, and ensure consistency. A high availability SLA of 99.999% is available for multi-cluster routing that spans three or more regions. This is in addition to 99.9 percent for single cluster instances.
4. Simple administration
Bigtable automatically performs updates and restarts invisible, and maintains high data durability. S