• November 23, 2020

    Use and Features of Google Big Query

    Google Big Query is a cloud-based enterprise data warehouse that offers rapid SQL queries and interactive analysis of massive datasets. Big Query was designed on Google’s Dremel technology and is built to process read-only data. The platform utilizes a columnar storage paradigm that allows for much faster data scanning as well as a tree architecture model that makes querying and aggregating results significantly easier and more efficient.

    Google big query’s cloud-based data warehouse and analytics platform uses a built-in query engine and a highly scalable serverless computing model to process terabytes of data in seconds and petabytes in minutes.

    Big Query is a fast, powerful, and flexible data warehouse that tightly integrated with the other service on the google cloud platform, it’s cost-efficient, offers used based pricing, and uses a serverless model.

    Big Query’s columnar Database:

    Big Query’s column-based storage service is behind the data warehouse’s speed and its ability to handle enormous quantities of data. Data in a most relational database is stored and accessed by row, and that’s an efficient storage scheme for a transactional database. For an analytical database, however, it’s more efficient to store data by column.

    The Google eco System:

    Big Query is part of Google Cloud Platform and integrates with other GCP service and tools. Big Query can process data stored in other GCP products, including cloud storage, the cloud SQL relational database service, The cloud Big table database NoSQL database, Google drive, and spanner, Google’s distributed database. Big Query supports standard SQL access, and can also integrate with business intelligence tools such as Tableau and Locker.

    Key Features:
    • Serverless:
      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.
    • Multi-Cloud Capabilities:
      Big Query Omni allows you to analyze data across clouds using standard SQL and without leaving Big Query’s familiar interface. It’s flexible, fully managed infrastructure allows your data analyst or data scientists to have a completely seamless data analysis experience.
    • Natural Language Processing:
      Data QnA makes it easy for anyone to access the data insights they need through NLP-all while maintaining governance and security controls. Based on Analyze (Google Research), Data QnA enables you to analyze petabytes of data via Big Query, and can be embedded where users work; chatbots, spreadsheets, BI platforms like Looker, or custom-built UIs.
    • Realtime Analytics:
      Big Query’s high-speed streaming insertion API provides a powerful foundation for real-time data analytics making your latest data immediately available for analysis. You can also leverage Pub/sub and dataflow to stream data into Big Query.
    • Automatic high availability:
      Big Query transparently automatically provides highly durable, replicated storage in multiple locations and high availability with no extra charge and no additional setup.
    • Standard SQL:
      Big Query supports a standard SQL dialect that is ANSI: 2011 compliant, which reduce the need for code rewrites. Big Query also provides ODBC and JDBC divers at no cost to ensure your current applications can interact with its powerful engine.
    • Federated Query and Logical Data ware Housing:
      Through Powerful queries, Big Query can process external data sources in object storage for Parquet and ORC open-source file formats, transactional database (Bigtable, Cloud, SQL ), or spreadsheet in the drive. All this can be done without moving the data.
    • Convergence of data warehouse and data lake:
      Run Open source data science workloads directly on Big Query using the storage API. The Storage API provides a much simpler architecture and less movement and doesn’t need to have multiple data copies of the same data.
    • Materialized View:
      Accelerate query performance and reduce costs within your environment with Big Query materialized views.
    • Data Governance and Security:
      Big Query provides strong security and governance controls with fine-grained controls through integration with Identity and Access Management. Rest assured knowing your data is encrypted at rest and in transit by default.
    • Foundation of AI:
      Besides bringing ML to your data with Big Query ML, integration with AI platform prediction and TensorFlow enable you to train powerful models on structured data in minutes with just SQL.
    • Foundation of BI:
      Big Query forms the data warehousing backbone for modern BI solutions and enables seamless data integration, transformation, analysis, visualization, and reporting with tools from Google.
    • Public Datasets:
      Google cloud public datasets offer a powerful data repository of more than 100 high-demand public datasets from different industries. Google provides free storage for all public datasets, and the customer can query up-to 1TB of data per months.
    • Big Query data transfer service:

      The Big Query Data Transfer Service automatically transfers data from an external data source, like Google marketing platform, Google Ads, YouTube, and Partner SaaS application to Big Query on a scheduled and fully managed basis. Users can also easily transfer data from Teradata and Amazon S3 to Big Query.

    Big Query charges for data storage, streaming inserts, and querying data, but loading and exporting data are free of charge. To know more about Big Query and its pricing information please visit the google cloud big query guide.

For 5+ years, we are reliable service providers to our customers with the essential goal of consistently delivering quality. Our strength lies in shared ideas and returns to the community.