DATA WAREHOUSING WITH BIGQUERY
Data Warehousing in Google Cloud. What makes BigQuery So Powerful?
Data warehouses contain large collections of both historical and current business data that help organizations make intelligent decisions. Data warehouse systems analyze and report structured and semi-structured data from multiple sources and systems such as customer relationship management, lead capturing, marketing automation, point-of-sale and more. What is Data warehousing? This is the process of collecting and managing data from diverse sources to provide business intelligence. “The Cloud” is a hot topic in the tech space, not just for its robustness when it comes to computing power but also in data warehousing. Google, being a data-focused company, has been forefront in the data warehousing, attributed to the launch of BigQuery in 2011, more on this later.
Benefits of Google Cloud Data Warehouse
Managing an on-premise data warehouse can be costly and involves mandatory maintenance. The on-premise data center may lack the capacity to store and process the enormous amount of business historical data that keeps growing every day. Below are some of the benefits of a Google Cloud data warehouse:
- Google Cloud Manages it for you – With your data warehouse in Google Cloud, you don’t have to worry about the daily operational costs. This allows your tech team to focus on growing your business.
- Scalability – it’s a fact that your business data will keep growing. This means you will need more storage capacity and more computing power to process the increasing data. Cloud data warehouses are built for scale. They adapt to your changing data needs and you don’t have to worry about purchasing new systems when your data grows.
- High Availability – Google Cloud guarantees better uptime because of the obligation to meet SLAs. Additionally, Google Cloud can recover better and faster from disasters than on-premise setups.
- Performance and Real-time insights – Google cloud supports querying data in real-time allowing your business to benefit from fast and intelligent decisions.
Integration with other Google Cloud services – with your data warehouse in Google Cloud, you can benefit from other Google Cloud services such as artificial intelligence, machine learning, and the extremely powerful BigQuery that we’ll talk about in the next section.
What makes BigQuery So Powerful?
BigQuery is Google Cloud’s fully managed, highly scalable, and serverless enterprise data warehouse that comes with a built-in query engine. It is managed in that Google manages its resource allocation, auto-scaling and ensures it’s available whenever you need it. It is serverless in that you don’t configure servers; instead, you only give it your workload, it does the analysis and gives you the computed results.
BigQuery is designed to run SQL queries that analyze large datasets reaching terabytes to petabytes of data and return results within seconds to minutes. It can analyze joins of datasets from completely different sources which is the basic definition of data warehousing.
BigQuery delivers high performance without having to create or rebuild indexes to reach considerable querying speed as you would have to do in normal Relational Database Systems like MySQL, Microsoft SQL Server, or PostgreSQL. Regardless of the size of the dataset you have, BigQuery scales up automatically to run your queries and then scales down to zero when done.
BigQuery supports standard SQL queries, so you can ship your queries from other relational databases and run them on the scalable BigQuery infrastructure. Furthermore, there are other inbuilt BigQuery functions to support modern data analysis requirements.
BigQuery provides powerful analytics by allowing integration with other business intelligence tools such as Looker, Google Data Studio, and Tableau. You can use these tools to create easy to understand visualizations and reports based on data held in BigQuery.
BigQuery is powerful because it is built on top of Google’s infrastructure with engineering capabilities such as separation of computing and storage, columnar storage, and support for nested and repeated fields. The separation of computing and storage in BigQuery ensures that any machine within the data center can take in data from any storage disk – this explains why BigQuery can analyze large datasets from various sources, running many queries concurrently. BigQuery data centers run on thousands of extremely powerful servers communicating at bandwidths reaching 10 GB/sec – the networking infrastructure makes the queries appear like they are running on one machine.
BigQuery integrates well with other Google Cloud Platform tools including but not limited to the following:
AutoML – can create machine learning models using the data in BigQuery.
Federated Queries – enables BigQuery to query data residing in various Google Cloud tools such as Cloud Storage, Cloud SQL, Bigtable, Cloud Spanner, or Google Drive spreadsheets without copying or moving data.
Cloud AI Platform – can train machine learning programs using data in Big Query.
Cloud Operation, formerly known as Stackdriver – allows you to monitor how your staff is using BigQuery and for data security.
Google Cloud Data Loss Prevention – a tool that helps you discover, classify, protect and mask sensitive data from BigQuery tables.