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Cloud Data Warehouses - Jul 31 2021

The CTO Guide to BigQuery

BigQuery is a powerful data warehouse solution designed for serverless, multi-cloud workflow. If you’re looking at BigQuery to solve some of the main problems in your business, this quick guide can help your decision-making.

What is BigQuery used for?

BigQuery is a cloud-based data warehouse solution provided by Google. It stores structured data derived from multiple sources in the cloud, so that data can be analyzed to generate actionable business insights.

To this end, BigQuery offers a scalable and multi-cloud data storage environment primed for data warehouse use cases. It also bundles a powerful analytics solution, security features, and fast ANSI SQL queries with no overhead, machine learning, and several other essential features that can supercharge your data warehouse implementation.

BigQuery is a fully-managed service that can function out of the box with no need for any additional installation or configuration. The core of the solution is Google Dremel, a distributed system meant to query very large datasets. Also, under the hood, the solution uses Google Jupiter, their internal data center network, and Colossus, Google’s implementation of a distributed file system.

BigQuery uses a columnar format to store data, which helps achieve excellent scan throughput and a high compression ratio. However, it can also be used directly with data stored in other Google services, including Google Drive, Google Cloud Storage, Google Cloud SQL, and BigTable.

Who uses BigQuery?

Businesses that need to operate a data warehouse for data analysis to generate important business insights can make use of BigQuery. Typically, this includes businesses that analyze a large amount of customer and market data to generate insights that can help streamline business strategies, refine products and services, and predict market trends for more successful marketing and sales efforts.

While BigQuery is a versatile tool, it might not be the perfect choice for certain use cases. For your particular use case, it can make sense to employ BigQuery if your workflow meets certain conditions. Follow these guidelines:

  • If your data does not change often, using BigQuery can provide a big boost in speed as the solution uses caching. For same or similar queries, BigQuery can use cached results in place of recalculating if the data is unchanged to yield faster results. It is also critical to remember that cached queries are processed without charge.
  • If you are running queries in a relational database that run for more than five seconds, BigQuery can be an apt choice. If most of your workflow hinges on simple queries like filtering or aggregation, BigQuery might not be the best choice as it focuses on more complex analytical queries that happen over large datasets. The larger your dataset and the more complex your queries, the more benefit you can get from using BigQuery.
  • If you want to mitigate the added load on your relational database, BigQuery can help. Repeatedly using heavy analytical queries on a relational database can cause performance issues. In fact, this is why many businesses are forced to scale up their servers. However, with BigQuery, you can just offload these queries to a third-party service and keep your principal relational database unaffected.

Is BigQuery a database?

BigQuery is a data warehouse solution, designed to store large amounts of structure data with built-in data analysis tools that can help generate business insight. A data warehouse is different from a database in many ways, although both are used to store data.

In most transactional databases, data is stored in rows. However, in an analytical data structure such as BigQuery, a columnar structure makes more sense. A columnar structure can process data faster and more efficiently, accessing only the required columns for a query.

For those looking for a NoSQL database solution, Google offers BigTable.

Does BigQuery use SQL?

BigQuery uses ANSI SQL for its query engine. Previously, BigQuery operated with its own non-standard dialect of SQL. Over time, it has also implemented support for standard SQL. Standard SQL support brings a lot of added features to the table and helps increase compatibility with many more use cases and platforms.

The previous standard has been christened Legacy SQL and users have the option of using either and migrating from one to the other. Standard SQL accommodates nested fields, arrays, more user functions, more subquery options, requests to external sources, and more JOIN conditions.

On top of that, the stricter grammar requirements of standard SQL mean that the chance for mistakes is reduced. Standard SQL code is also easier to read and edit and requests typically run faster.

Why is BigQuery so fast?

BigQuery is extremely fast in many ways due to its architecture and implementation. Firstly, it is an implementation of Dremel, which is inherently a fast architecture due to its implementation of a tree architecture and columnar storage. This architecture creates the opportunity for better compression ratios and higher scan throughput. The tree structure implies that it only takes a few seconds to dispatch queries and aggregate results across multiple machines.

The query engine makes use of ANSI SQL and is optimized for high speed and low overhead. Finally, BigQuery makes use of Google Jupiter network to remove bandwidth bottlenecks for an overall speed of 1 petabit/second of bandwidth.

Is Google BigQuery free?

Pricing for BigQuery is sectioned into two components- pricing for storage and pricing for data analysis. Companies can opt for a flat-rate pricing model or an on-demand pricing model depending on their needs to achieve efficiency. Short-term, monthly, and annual flat-rate options are available. In addition, there can be additional costs for data extraction and data ingestion.

For those looking to use BigQuery for smaller projects, there is a free tier that provides a limited amount of free usage and free operations per month. In the free tier, the first 10GB of usage per month is processed free of cost. In addition, the first 1 TB of data in terms of queries processed every month is also free.

How long does it take to implement BigQuery?

BigQuery is set up in a way that helps minimize adoption time by providing a simple onboarding process. The setup process is simple and allows you to quickly create data ingestion pipelines and integrations. The analysis process is significantly faster than most other similar solutions and the lack of bandwidth bottlenecks make the service a great choice for those looking to quickly set up and start using a data warehouse solution.

Due to the fact that BigQuery works out of the box with minimal interference and its seamless integration with other Google services, the time to get BigQuery up and running can be significantly less than other popular data warehouse solutions. The caveat is that getting your data in order, modernized and aligned with your overall data strategy might take some time. 

When do I need a BigQuery service provider?

If you are looking to implement a data warehouse for structured data analysis and your focus is on speed, flexibility, features, and high ROI, Google BigQuery can be a meaningful choice for you. It can make even more sense if you are already using Google Cloud solutions as there are a myriad of integration opportunities. 

Have questions? We help companies like yours, every day.

Email us at hello@nextphase.ai

 

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About NextPhase.ai

NextPhase.ai is a data cloud services provider specializing in Snowflake, cloud data management and analytics technologies. We accelerate enterprise digital transformation initiatives by leveraging our innovative cloud data management technology, “NextPhase.ai DATAFLO” to optimize and rationalize disparate enterprise data into relevant insights. “DATAFLO” is designed to automate the lifecycle of data management transformation using AI and ML along with expeditious on-ramps to the Snowflake data cloud infrastructure. NextPhase.ai provides a range of technology consulting services for the Financial Services, Biotech and Technology industry sectors combining our platform-based services, seasoned talent, and industry proven methodology so our customers can harness more from their data. We are a Silicon Valley based company with global presence having delivered high value service engagements for numerous Global 2000 enterprises.

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