How to upload data to Google BigQuery

Google BigQuery (GBQ) allows you to collect data from different sources and analyze it using SQL queries. Among the advantages of GBQ are its high speed of calculations — even with large volumes of data — and its low cost.

Why do you need to load data into one storage? If you want to use end-to-end analytics, use raw data for creating reports, and measure the efficiency of your marketing, then you should use Google BigQuery.

If you need to analyze terabytes of data in seconds, Google BigQuery is the easiest and most affordable choice. You can learn more about this service by watching a short video on the Google Developers YouTube channel.

Creating a dataset and table

Before you upload any data, you need to create a dataset and table in Google BigQuery. To do this, on the BigQuery home page, select the resource in which you want to create a dataset.

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In the Create dataset window, give your dataset an ID, select a data location, and set the default table expiration period.

Note: If you select “Never” for table expiration, the physical storage location will not be defined. For temporary tables, you can specify the number of days to store them.

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Next, create a table in the dataset.

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It’s ready! Now you can start loading data.

Upload data with Google Sheets (OWOX BI BigQuery Reports Add-on)

If you need to upload data from Google Sheets to Google BigQuery, the easiest way to do that is to install the free OWOX BI BigQuery Reports Add-on.

You can install this add-on directly from Google Sheets or from the Chrome Web Store.

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After installing it, a dialog box will appear with tips and permission requests.

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Now it’s time to go back to Google Sheets. To upload data to BigQuery, just select Upload data to BigQuery from the Add-ons –> OWOX BI BigQuery Reports menu.

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Specify the project, dataset, and name of the table to upload the data to. And that’s all :)

An undeniable advantage of the OWOX BI BigQuery Reports Add-on is its ease of use. You can also use the add-on to set up scheduled reports.

To build reports based on accurate raw data from all sources and automatically upload them to Google BigQuery repository, we recommend using the OWOX BI Pipeline service.

With Pipeline, you can set up automatic data collection from advertising services as well as call tracking and CRM systems. This allows you to quickly and easily get ready-made sets of complete data from the sources you select.

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Just select data sources and grant access; leave the rest to OWOX BI.

With OWOX BI, you can build reports for every taste and need, from ROI, ROPO effect, and cohort analysis to LTV and RFM analysis.

#owox-bi #how-to #data #google-big-query #data analysisa

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How to upload data to Google BigQuery
Siphiwe  Nair

Siphiwe Nair


Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Gerhard  Brink

Gerhard Brink


Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.


As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

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Sid Schuppe


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Using data to inform decisions is essential to product management, or anything really. And thankfully, we aren’t short of it. Any online application generates an abundance of data and it’s up to us to collect it and then make sense of it.

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Nothing is more powerful than data democracy, where anyone in your organization can regularly make decisions informed with data. As part of enabling this, we need to be able to visualize data in a way that brings it to life and makes it more accessible. I’ve recently been learning how to do this and wanted to share some of the cool ways you can do this in Google Data Studio.

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In GCP , BigQuery is serverless way of doing petabyte scale analytics. This blog explains about BigQuery data warehouse solution on GCP.

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BigQuery is a data warehouse that is built for the cloud. Its google proprietary data warehouse solution on Google Cloud Platform.

BigQuery is Serverless that means as a customer we don’t have to configure/manage any servers & storage.It will be done behind the scene by google. as a customer, our job is to upload the data and query that means which just focus on business rather than thinking about infrastructure.

BigQuery is not a transactional database like Mysql or Oracle. BigQuery is designed for analytical workloads.

For Example, Query like below is called an analytical query because its purpose is to analyze the data and provide some calculative results like count, max, min, avg, etc.

Here we trying to find titles and total_views for each Wikipedia page.

SELECT title,

count(views) as total_views
DATE(datehour) = “2020–04–18”

Analytical queries are very useful in reporting and business intelligence because it provides insights from data based on which Business side can make the tactical decision for the company.


Being Serverless we actually don’t need to know about underlying architecture but in knowing it would be helpful for us to optimize our query, cost & performance in some scenarios.

BigQuery is built on top of Google Dremel technology which is used inside google since 2006 in many services in production. (Please refer reference section for the paper)

Dremel is google’s interactive ad-hoc query system which is designed to query read-only data. BigQuery uses Dremel for its execution engine.

Apart from Dremel BigQuery uses Google’s innovative tech like Borg, Colossus File Syste, Jupyter network, and Capacitor.

#introduction-to-bigquery #bigquery-for-beginners #gcp-data-warehousing #data-warehouse #google-bigquery #data analysis

Cyrus  Kreiger

Cyrus Kreiger


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The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.

Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.

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