In the world of technology, being a big company means dealing with big data, and working with large amounts of data isn’t possible with traditional data processing techniques. Think of billions of rows on a spreadsheet, and use cases ranging from terabytes to petabytes of data. In some cases, these data warehouses can be more than an exabyte.
Social media platforms like Facebook and Twitter generate huge amounts of data every second. That data is constantly updated and processed in real time or near real time. How can that much data be stored and analyzed? That’s where Google BigQuery comes in. Social media platforms aren’t the only companies that work with extremely complex and large datasets. Global financial entities like HSBC, the media giant The New York Times, and the telecommunications company Vodafone all use BigQuery.
Google has built the infrastructure capable of handling big data needs, and companies of all sizes can use the same infrastructure that Google uses for itsuser-facing products, like Google Maps, Google Drive, and Google Workspace. Google BigQuery is one part of that infrastructure. It integrates seamlessly into the Google Cloud Platform (GCP) ecosystem, a suite of cloud computing tools and services developed to handle data sources of all sizes, all the way up to these huge corporate enterprises.
Google BigQuery is a powerful, serverless, fully managed, and highly scalable cloud-based data warehousing service for data storage. It allows companies to analyze vast amounts of data using machine learning models and process huge workloads using Google’s CPUs, all in real time. It has a pricing model that scales with a company’s needs and is competitive with similar services from Microsoft Azure, Amazon Redshift (part of AWS), and IBM’s cloud platform.
Google BigQuery and a place for big data to live
BigQuery, much like the other tools and services offered under the Google Cloud Platform suite, takes advantage of Google’s scalable and reliable infrastructure. Being cloud-based and serverless means companies don’t need to store data in their data centers, eliminating the need for server setup and maintenance. BigQuery is also multi-cloud, so a company doesn’t have to use GCP only. It can also use cloud services from other providers, such as AWS and Microsoft Azure.
BigQuery supports data transfer in several file formats like the columnar storage formats Parquet and ORC, JavaScript JSON, binary file format Arvo, exports from MySQL, simple CSV orGoogle Sheets files, and other relational databases. It works well with backups from Google’s NoSQL services, Datastore, Firestore, and Cloud Bigtable.
It’s also relatively easy to create data pipelines to move, ingest, and analyze data from different systems, such as another provider’s cloud data warehouse, imported datasets, and stream data connectors. Google Dataflow is a managed service provided by GCP for building these data processing pipelines. It is used with BigQuery to process and transform data before loading it into the BigQuery tables. After each connector is set, developers can automate the movement of future data through these pipelines.
If data comes from multiple sources, and you need to perform ETL (Extract, Transform, Load) operations, you can use Google’s Dataflow service or several other data integration platforms. In this case, integration combines and consolidates data from different sources into a unified view. For example, BigQuery supports tree architecture, so a developer may want data from one source to be nested in data from another source. The data can be combined and re-arranged in hierarchical, parent-child relationships.
As long as a schema defines the organization of data within the table, it doesn’t matter where the data comes from, how it was previously organized, or what format it’s in. Now that all that data has a place to live, in Google Cloud storage, and a structure or schema, what can businesses do with it? Well, Google is a powerhouse in cloud computing, and there are more options than data aggregation and running standard SQL queries.
Dremel is BigQuery’s powerful execution engine
Data analytics is a given since that’s why these huge datasets are generally collected, and that’s done by running something called a query. It’s a fundamental aspect of data analysis and working with databases. To query data is to send a request or command to retrieve specific information or perform operations on a dataset.
A company can run queries powered by the underlying technology Dremel, the query execution engine. Dremel is designed to handle these massive structured and semi-structured databases quickly and with low latency. It achieves this using a columnar storage format and an execution engine optimized for complex, on-demand, ad-hoc queries.
Rather than scanning an entire table, Dremel scans the columns required for a specific query. It does this through indexing and aggregation. While, in some ways, the indexing is similar to indexing web pages or files on a computer, the key difference is that Dremel uses a technique called columnar projection.
Columnar projection reads through the data and creates summaries for each column in the dataset. Then it glances at these summaries when running a query rather than reading through the data in every column again. In a way, columnar projections are like metadata that help the search engine understand insights about the data so that it knows where the data you are looking for is faster.
Another reason BigQuery can handle large-scale analytical workloads is that Dremel’s query execution is spread across a massive cluster of computers. It partitions and processes data in parallel using Google’s computing infrastructure. Dremel’s tree architecture spans multiple levels of servers, utilizing the power of multiple CPUs to execute queries at the same time and provide a fast query performance.
By dividing the original workload into smaller subtasks and processing them independently across a collection of interconnected machines or nodes, tasks can be performed efficiently. Another benefit is a high degree of fault tolerance because if one node fails, the task can be redistributed, and the processing can continue. This type of distributed parallel processing also makes for a scalable system that can grow to accommodate a business as its data needs grow.
Being able to perform data analysis on terabytes of data and deliver results within seconds or minutes, BigQuery makes business intelligence easy. Many companies couldn’t achieve this on their own because the resources needed to compute at this level require so much infrastructure and maintenance. Even large enterprises that could build this infrastructure prefer to use big data suites, like Google Cloud Platform, so they can focus on what they do best and leave the back-end server management to the experts.
BigQuery data visualization and dashboards
BigQuery doesn’t have built-in dashboards to visualize queries. Still, it integrates with various visualization and dashboarding tools, like Google Data Studio, and other popular platforms like Tableau, Looker, and Microsoft’s Power BI. These tools provide user-friendly interfaces that can help a company customize interactive reports, visualize data using charts, and share insights with others in a way that makes more sense to them.
BigQuery integrates seamlessly with Data Studio, and because of the computing power under the dashboard, visualization of data can happen in real time. There’s always a database behind the bar charts, line graphs, pie charts, maps, and other ways we typically see data represented. With tools like Data Studio, these visual elements can be made easily by choosing the data tables or views to visualize, applying filters, defining what data to represent, and then using a drag-and-drop interface to arrange everything on a canvas.
In TV and movies, we’ve all seen someone present static charts on large paper or clicking through a PowerPoint presentation. These days, visualization can be more than a static chart. With these visualization tools, you can present simplified and easy-to-ingest information to stakeholders, then interactively drill down to specific data points, explore different angles or date ranges, or prepare drop-down menus to get into granular details with the technical team.
Machine learning capabilities in BigQuery
Machine learning is everywhere, and AI learns through model training using large datasets. The data AI learns from is always large and diverse, typically containing many examples of the same instances relevant to the specific AI task. During training, the model learns by repeatedly making predictions based on the dataset and testing those predictions against the true outputs of the dataset. The model then iteratively adjusts its internal parameters to make better and more accurate predictions.
The size and diversity of these datasets provide a model with a broad understanding of its job so that it can perform complex tasks, make predictions, and extract insights that might be difficult or impossible for humans to find. The larger the dataset, the more intelligent the AI model can become. It’s big data suites like Google Cloud Platform and tools like BigQuery that allow for datasets large enough that we are now seeing vast improvements in machine learning.
BigQuery ML (Machine Learning) was officially released in 2019. It allows users to create and execute machine learning models in BigQuery using the Google Cloud console. Users can use SQL queries and their existing SQL tools and skills without knowing ML-specific programming skills and ML frameworks.
Google’s goal with BigQuery ML is to democratize machine learning and make it more accessible. It isn’t democratized yet, and BigQuery ML is an additional cost on top of the base Google Cloud Platform suite. The pricing model is good for enterprise customers. However, free usage through theGoogle Cloud Free Tierhas restrictive resource caps that are limiting for small businesses or everyday people trying to learn more about machine learning.
Google Query APIs and communication between software apps
Interacting with Google Query resources programmatically is done through BigQuery APIs (application programming interfaces). APIs enable communication and interaction between software applications and systems like BigQuery. They define a set of rules and protocols for how an app and a system should interact.
The BigQuery API allows developers to manage BigQuery resources, such as datasets, tables, and jobs. It provides methods for creating, updating, and deleting these resources, as well as executing SQL queries and retrieving query results. Google also provides client libraries and SDKs (software development kits) for various programming languages, making it easier for developers to interact with BigQuery through APIs using their preferred programming language.
REST APIs (Representational State Transfer), for example, are designed based on HTTP protocols for communication and are widely used in developing web services and applications. BigQuery’s REST API allows web browsers or mobile apps to request and manipulate database resources on the server using standard HTTP methods like GET, POST, PUT, and DELETE. The server then responds with the requested resource or performs the requested operation.
A real use case for an Android app that utilizes this API is Google’s voice search, which uses exabytes of audio data and powerful machine learning to find things for you in seconds. It can sometimesdetect music samplesless than a second long. Processing and analyzing that amount of audio data on your mobile phone, even if it’sone of the best on the market, is impossible. Similar services like Shazam or SoundHound have huge databases of music they need to reference when recognizing a song for you. They could leverage Google BigQuery to store and analyze the data while focusing on their user interface and your playlists.
The impact of BigQuery on businesses and everyday people
With the exponential growth of data and the need for real-time analysis, the importance of tools like Google BigQuery in the world of big data is significant. Even small businesses can leverage advanced data processing capabilities that were once exclusive to larger organizations as long as they havebasic SQL knowledgeand collected enough data to work with. Google BigQuery is vital in unlocking the potential of big data for businesses of all sizes and is designed to scale as a company grows.
BigQuery integrates seamlessly with the rest of the Google Cloud Platform and various data visualization and dashboarding tools. This enables businesses to transform raw data into actionable insights. Analyzing and visualizing large datasets empower organizations to make data-driven decisions, identify trends, and uncover valuable business intelligence.
Companies like social media platforms, financial institutions, media giants, and telecommunication companies rely on BigQuery to store and process massive amounts of data generated by user interactions. This invisible impact on everyday people becomes evident through personalized experiences and improved services. Leveraging and processing these massive datasets enable these companies to deliver personalized experiences, targeted advertising, improved services, and relevant content to individuals.
Whether you like it or not, companies know a lot about you. We’ve become datasets that are nested within larger databases, and all it takes to make a prediction about us is a simple SQL query.
