Vlada Malysheva,Creative Writer atOWOXBI
Torun advanced analytics, you need data towork with. However, once you’ve decided tocollect data, you need todecide how tostore it. Should you choose astandard data warehouse ora data lake? Inthis article, wediscuss why Google BigQuery asa data lake isthe best choice.
What is a data lake?
Adata lake isthe next level inthe evolution ofdata storage. According toTechTarget, “a data lake isa storage repository that holds avast amount ofraw data inits native format until itis needed.” Data lakes appeared because new technologies and business requirements necessitated new approaches tostoring and processing information.
But what exactly can you dowith adata lake?
With adata lake, you can collect and store any raw, unstructured data from any source. You don’t need tofirst define the structure and schema ofthe data; you can process the data asrequired and build your business intelligence solutions onit.
Today’s customer journeys are more fragmented than ever. And all data onthese journeys needs tobe stored somewhere without the risk ofdata loss. You never know what kind ofdata you’ll need ina year. Adata lake copes with this task.
Tobetter understand how adata lake works, let’s compare atraditional data warehouse toa data lake.
Data warehouse and data lake differences
Imagine you want tobuild acastle with towers. For this task, you can choose between two sets oftools: cubes identical insize but invarious colors ora 250-piece LEGO set with bricks ofall shapes, sizes, and colors.
Asyou’ve likely guessed, the basic cubes ofthe same size represent standard data storage. Tostore data ina data warehouse, you must first bring itinto the same format and structure. Inother words, you need to:
- spend time preprocessing the data
- build your castle exclusively from uniform cubes
Ifyour business isjust taking its first steps, then cubes (ordinary data storage) are useful.
But ifyou want tobuild aDisney castle with turrets, windows, weather vanes, and trebuchets, you need aLEGO set (i. e. adata lake). The undeniable advantage ofa data lake lies inits ability totake inraw, unstructured data from everywhere. You can put all the information you have inyour data lake: data from advertising services, mobile applications, call tracking and CRM systems, websites, vending machines. Then you can take the data you need and build reports inthe way your business needs. Tempting, isn’t it?
Additionally, with adata lake, you don’t need tospend time preprocessing data. You just need toset upconnectors between data sources and the data lake once. Then you can create any reports. The most exciting thing isthat adata lake allows you tocreate dashboards with real-time updates — precisely what you need toinstantly respond tocritical changes inyour metrics and KPIs!
Ifyou’re looking for aconvenient connector for transferring data toGoogle BigQuery, werecommend OWOX BIPipeline. Itcombines data from Google Analytics, advertising services, websites, offline stores, call tracking systems, and CRM systems into Google BigQuery.
BOOK A DEMO
Ifyou want tobuild reports based onGoogle BigQuery data inyour favorite Google Sheets oryou want totransfer data from Google Sheets toGoogle BigQuery, werecommend using the free and convenient OWOX BIBigQuery Reports add-on.
The BigQuery Reports add-on ispopular for many reasons:
- It’s free, safe, and secure
- Itdoesn’t require you toupload data asCSV files oruse paid third-party services
- Ituses only Google’s official APIs
You can find more details about this OWOX BIadd-on here.
Why is Google BigQuery the perfect data lake for marketing?
Now that we’ve figured out the difference between data storage and adata lake, weneed tochoose the best variant. There are many data lake solutions onthe market, but for marketing, there’s only one best option — Google BigQuery. Let’s briefly describe what Google BigQuery isand why it’s the best solution for storing marketing data.
It’s difficult toimagine amarketer who doesn’t work with Google Ads, Google Analytics, YouTube, and other Google services. Google isa real monster ofmarketing and advertising. And Google BigQuery ispart ofGoogle’s infrastructure. Insimple words, this means native integrations.
Google iscontinuously developing its cloud services platform, including BigQuery. Soyou don’t need toworry that this service will beabandoned and cease tobe supported and updated. Among its other advantages, Google BigQuery issimple and fast, and avast number ofspecialists can work with it. Italso comes with ready-made sets ofSQL queries soyou can get useful insights from your collected data.
And let’s not forget about the current problems ofmarketers: how toquickly respond tochanges inthe market and how tomanage bids and segment automation inreal time. Also, let’s not forget that your success significantly depends onhow you can automate and personalize your marketing. Google BigQuery works with machine learning (ML) and artificial intelligence (AI), which help you analyze and automate your marketing bysegmenting audiences, searching for useful insights, and doing many more things tomake your life easier.
The bottom line isthat Google BigQuery isa fully managed serverless data warehouse that enables safe and scalable analysis ofpetabytes ofdata. For more than adecade, Google BigQuery has been developing, improving, and providing marketers and analysts with aconvenient interface and extensive capabilities.
Ifyou’re already sold onBigQuery, you can immediately jump tothe conclusions ofthis article orgo read other articles about setting upand working with BigQuery. Ifyou’re still onthe fence, here are some reasons why you should give BigQuery atry.
Video help section
Features of Google BigQuery
Let’s take acloser look atwhy Google BigQuery isthe best choice for today’s marketers.
- Integrations. BigQuery ispart ofthe Google Cloud Platform (the leader inData Management for Analytics according toForrester Research), which means native integrations with other Google products including Google Analytics and Google Ads.
- Data processing speed. BigQuery was designed toenable real-time analysis ofany type ofdata. You can use SQL queries with ease and atany scale.
- Noservers. Using the BigQuery cloud service doesn’t require any attachments from you. Inaddition, nomatter where your employees work, they’ll always have secure access todata.
- Data security. All data inBigQuery isprotected according toGoogle’s standards.
- Cost. All users receive 10 GBfor storage and upto 1 TBof requests per month for free. Inaddition, new users receive $300 for 90 days topay for services onthe Google platform. For more information, see Google’s guide toBigQuery pricing and cost controls.
- BigQuery ML. With this service, experts can build prediction models onboth structured and semi-structured data directly inside adata lake.
Tosummarize, Google BigQuery ispart ofa large ecosystem that’s continuously growing and developing. You can use itto apply machine learning and discover emerging data patterns and test new hypotheses. This will lead totimely insights into how your business isperforming, which will enable you tomodify your processes for better results.
- Get started with Google Cloud Platform — Aninteractive tutorial tolearn the basics ofthe Google Cloud Platform
- Overview ofthe main Google BigQuery features — Practice writing requests for marketing analysis; look atthe main functions ofBigQuery and see their possibilities using specific examples; learn how towrite basic queries and test them ondemo data
- Connecting BigQuery and Google Sheets — Find out how tobuild any report orgraph inGoogle Sheets based ondata from GBQ without needing toupload data asCSV files oruse paid third-party services
- Modernize your data warehouse with BigQuery — Discover how abrick-and-mortar and online retailer uses advanced analytics inBigQuery tobetter forecast demand and optimize their operations inreal time
- Building ane-commerce recommendation system using BigQuery ML
Marketing analytics, with its data-based conclusions and forecasting, isa necessity for any modern business. It’s nolonger atoy for the rich but anecessary and useful tool for business development and progress. However, tofully use and benefit from advanced analytics, it’s essential tocreate abasis for it.
Toimplement new tools, machine learning, and various methods for optimizing advertising campaigns, abusiness needs tomake decisions based onthe data it’s collected. For marketing departments, the best solution for storing data isa data lake — specifically, the popular and convenient Google BigQuery.
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How does GBQ compare to traditional data warehouses?
GBQ offers several advantages over traditional data warehouses, including: pay-per-use pricing, high-speed querying, no need for upfront hardware or software investments, and easy integrations with other Google Cloud Platform services.
What are the benefits of using GBQ for data lake analytics?
Some of the benefits include: fast query performance, scalability, ease of use, cost savings, versatility, and advanced machine learning capabilities.
How does Google BigQuery (GBQ) fit into a data lake solution?
GBQ is a cloud-based data warehouse that can integrate with a data lake to provide a powerful analytics platform. It allows businesses to store and query massive datasets, while seamlessly integrating with other Google Cloud Platform services for processing and analysis.