BigQuery is considered an example of infrastructure as a service (IaaS). This tool can be used with Apache Hadoop or other frameworks to handle large data sets.
Additionally, it also provides a REST API that uses the Representational State Transfer (REST) model for open and transparent collaboration. Using data in BigQuery means that the data needs to be uploaded first to Google Storage.
It has an API that can be used to integrate this process into the data analysis. Not to mention, it is compatible with SQL queries. And therefore, it can be used with Google Apps Script, Google Spreadsheets, and other Google services.
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Accelerate time-to-value with a fully managed and serverless cloud data warehouse that is easy to set up and manage and doesn’t require a database administrator. Jump-start data analysis cost-effectively and uncover meaningful insights to stay competitive. With BigQuery, you can also;
- Quickly analyze gigabytes to petabytes of data using ANSI SQL at blazing-fast speeds, and with zero operational overhead
- Efficiently run analytics at scale with a 26%–34% lower three-year TCO than cloud data warehouse alternatives
- Seamlessly democratize insights with a trusted and more secure platform that scales with your needs
What is BigQuery?
BigQuery is a web service from Google that is used for handling or analyzing big data. It is part of the Google Cloud Platform. As a NoOps (no operations) data analytics service, it offers users the ability to manage data using fast SQL-like queries for real-time analysis.
Unlock insights with real-time and predictive analytics: Query streaming data in real-time and get up-to-date information on all your business processes. Predict business outcomes easily with built-in machine learning and without the need to move data.
Access data and BI tools with ease: Securely access and share analytical insights in your organization with a few clicks. Easily create stunning reports and dashboards using popular business intelligence tools, out of the box.
Protect your data and operate with trust: Have peace of mind with the BigQuery robust security, governance, and reliability controls. BigQuery offers high availability and a 99.9% uptime SLA. BigQuery provides encryption by default and also supports customer-managed encryption.
What are the Key Features of BigQuery?
As a matter of fact, storing and querying massive datasets can be time-consuming and expensive without the right hardware and infrastructure. It’s an enterprise data warehouse that solves this problem by enabling super-fast SQL queries. Using the processing power of Google’s infrastructure.
Simply move your data into BigQuery and let it handle the hard work. You can control access to both the project and your data based on your business needs, such as giving others the ability to view or query your data.
You can access BigQuery by using the Cloud Console or the classic web UI, by using a command-line tool, or by making calls to the BigQuery REST API using a variety of client libraries such as Java, .NET, or Python.
There are also a variety of third-party tools that you can use to interact with BigQuery, such as visualizing the data or loading the data. Below are the other Key Features of the BigQuery;
BigQuery BI Engine (beta)
How is BigQuery Commonly used?
Basically, it unlocks the full potential of data warehousing in the cloud for a broad array of tools and partners. From data integration to analytics, Google Cloud partners have integrated their industry-leading tools with BigQuery for loading, transforming, and visualizing data.
With serverless data warehousing, Google does all resource provisioning behind the scenes, so you can focus on data and analysis rather than worrying about upgrading, securing, or managing the infrastructure.
Run open-source data science workloads (Spark, TensorFlow, Dataflow and Apache Beam, MapReduce, Pandas, and scikit-learn) directly on it using the Storage API. The Storage API provides a much simpler architecture and less data movement and doesn’t need to have multiple copies of the same data.
Data warehouse modernization
Migrate your on-premise legacy data warehouse to an agile, cloud-based data warehouse solution.
Bearing in mind, it supports a standard SQL dialect that is ANSI:2011 compliant, which reduces the need for code rewrites. Also, it provides ODBC and JDBC drivers at no cost to ensure your current applications can interact with its powerful engine.
Through powerful federated queries, it can process external data sources in object storage (Cloud Storage). For example, Parquet, and ORC open-source file formats, transactional databases (Bigtable, Cloud SQL). Or spreadsheets in Drive — all without duplicating data.
In reality, focus on insights instead of buying and maintaining your own hardware and database servers. Jump-start your modernization journey with easy-to-use tools and a global partner support system. Learn more.
Migrating data from Teradata
It transparently and automatically provides highly durable, replicated storage in multiple locations and high availability with no extra charge and no additional setup.
The combination of the BigQuery Data Transfer Service and a special migration agent lets you copy data from an on-premises data warehouse environment such as Teradata to BigQuery.
The on-premises migration agent communicates with the BigQuery Data Transfer Service to copy tables from your Teradata environment to BigQuery. And you can monitor recurring data loads to BigQuery by using the BigQuery Data Transfer Service’s web UI.
With its separated storage and compute, you have the option to choose the storage and processing solutions that make sense for your business and control access and costs for each.
Automatically, it replicates data and keeps a seven-day history of changes. Allowing you to easily restore and compare data from different times.
Migrating from Amazon Redshift to BigQuery
The BigQuery Data Transfer Service allows you to copy your data from an Amazon Redshift data warehouse to BigQuery.
The service will engage migration agents in Google Kubernetes Engine and trigger an unload operation from Amazon Redshift to a staging area in an Amazon S3 bucket. Then the BigQuery Data Transfer Service transfers your data from the Amazon S3 bucket to BigQuery.
Eventually, its high-speed streaming insertion API provides a powerful foundation for real-time analytics. Above all, making your latest business data immediately available for analysis. You can also leverage Pub/Sub and Dataflow to stream data into BigQuery.
By the same token, bring all your marketing and customer data together to get a clearer picture of the customer journey. In the end, predict marketing and business outcomes, and create a more personalized experience for your customers. Learn more.
However, it charges for data storage, streaming inserts, and querying data, but loading and exporting data are free of charge. For detailed pricing information, please view the pricing guide.
Google Ads Data Transfer Service
Finally, working in the same marketing tech stack has its benefits. There are several ways to regularly schedule data imports to BigQuery, but most require some additional setup or code.
However, for many of its own products (Ads, YouTube, Display and Video 360, etc.), Google has created a simple solution. In particular, to set up regular imports with just a couple of clicks.
For the convenience of fresh reliable Ads data in BigQuery, Google charges a monthly fee for each External Customer ID imported with the transfer. It’s just five easy steps to enable the transfer.
It’s important to realize, this blog page documents production updates to BigQuery. Therefore, I recommend that BigQuery developers periodically check this list for any new announcements. Particularly, for the BigQuery;
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