Data analytics is important for all companies nowadays no matter their size, industry, or goals. Making data-driven decisions will help you significantly improve your company’s internal and external processes, reduce costs, increase your sales and ensure your customers’ satisfaction. There are many tools available to make business insights more accurate.
Many companies are deciding to operate serverless, using cloud-based solutions. Analytics can also be performed this way. BigQuery is now getting more popular every day as it leverages cloud architecture and can work with various types of data and ingestion models, which allows for more dynamic data storage. This solution is intuitive, fully managed and efficient. Are you wondering if you should consider using it to support your company’s data analytics?
In short about BigQuery
Big Query is a cloud-based serverless data warehouse that helps to ingest, store, analyse and visualize massive amounts of data. It gives users the possibility of using data warehousing as a service and paying only for the data being processed. As a part of the Google Cloud Platform it can be integrated with other Google services and tools and process data stored in any other GCP product.
Under the hood, Big Query has a few of Google’s cutting edge technologies:
- Borg – a cluster manager that can take care of hundreds of thousands of jobs across a number of clusters, each with up to tens of thousands of machines.
- Colossus – the newest generation distributed file system, which handles replication and data recovery.
- Jupiter – an extremely fast network connection that makes it possible to separate storage and compute.
- Dremel – a scalable query system for analysis of nested data.
You can communicate with Big Query via a user-friendly interface, a command line tool or by using client libraries for most programming languages (Python, Java etc.)
Big Query SQL Standard syntax
Structured Query Languages (known as SQL) are used for retrieving, adding and modifying data. Big Query supports ANSI:2011 compliant Standard SQL, which means the query syntax is widely used all around the globe. You don’t need to learn another complex language to manage and analyze your data.
Batch and streaming processing
Big Query enables work on already existing data (via batch) and real-time data (via streaming).
Batch is a load from cloud or local storage and the source file can be in Avro, CSV, JSON, ORC, Parquet or Firestone format. When you need to capture real-time data – it can be streamed directly into BigQuery. There are many options in Big Query to organize, store, analyze and present the streamed data.
Machine learning – Big Query ML
Google has announced that BigQuery can be used to design and deploy machine learning-based models on structured and semi-structured datasets. Thanks to that, experts can perform predictive analytics on data with machine learning technology. So, does BigQuery actually enable users to gain ML-based useful business insights?
BigQuery ML provides one, single environment for storing data and performing data analytics, including predictive analytics. In order to scan large volumes of datasets, it uses gradient descent methods to redesign the existing BigQuery engine for BigQuery ML.
Multi-cloud – Big Query Omni
This flexible solution makes Big Query a multicloud tool. Via a single Big Query UI with Standard SQL you can access data stored in other public clouds such as Amazon or Azure(coming soon). No copying of data is needed.
A fast in-memory engine for analyzing data stored in Big Query in little time with high concurrency, it makes it possible to transfer and present data in most popular BI tools such as Data Studio, Tableau and Power BI.
Is BigQuery good for marketing analytics purposes?
BigQuery is often used for storing marketing data. Why this solution? There is no marketer in today’s world that doesn’t work with tools such as Google Ads, Google Analytics, YouTube or other Google services. BigQuery is part of the Google infrastructure, and it can be used by a wide variety of specialists, so it is a great choice for marketing purposes.
BigQuery provides businesses with analytics (including real-time analytics) that can help them react to changes in the market, reduce costs when possible and improve processes within the company. It can be also used for automating and personalizing marketing campaigns, for example, by segmenting audiences using machine learning and artificial intelligence.
What are the economic advantages of using BigQuery?
If you decide to choose BigQuery you will be able to choose from a range of pricing options which are adjusted for the capabilities of companies of any size. This means that you don’t have to run a huge enterprise to benefit from BigQuery. There are two main pricing models:
- On-demand pricing – pay for each executed query depending on the bytes processed
- Flat-rate pricing – pay for purchasing slots (virtual – machines for computing power) – the more slots you have the faster your jobs run, but the price stays stable.
It is quite important that BigQuery eliminates the need to maintain hardware as it works in the cloud. It is also intuitive and simple to use with standardized SQL, which means that you don’t need to spend a lot of time and money learning how to use and configure a new tool.
The possibility to scale effectively when needed also positively affects a company’s budget. If you can scale up and down in a short time, you can leverage opportunities to improve sales by acting based on real-time analytics. You also don’t need to spend a lot of money to extend your hardware resources.
To sum up
BigQuery is a cloud warehouse that should be taken into account as a main component of modern analytics in today’s companies. Choosing a serverless service eliminates the need for large engineering, You can focus completely on analytics. Its Machine Learning modules, easy integration with other clouds and components make BigQuery a serious market player – a service that can be tailored and scaled to business needs.
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