Advanced Google BigQuery API by dJAX, integrate all your invaluable data from numerous data sources; e-commerce, offline files, media and marketing, data destination for all your business needs. Scale from point-to-point integration up to complex workflow that can use conditional logic and can also process billions of tasks in just milliseconds. Become limitless in data handling with advanced API enhance performance in the data industry. The BigQuery API offered by dJAX has access to all the features BigQuery has to offer; including storage solution build to manage modern data-driven marketing. An inexpensive solution for storing and querying huge datasets in the absence of hardware and infrastructure. Ideal API for assured improvement in data handling.
Powerful API with data normalization capabilities, data loaded will be provided in a consistent and immediately workable format. Access to granular data at any given moment, no coding necessary. Connect with previously soiled data streams and self-empower for deeper analysis.
Integrate and obtain quality data with ease and access to all the advanced features of BigQuery for enhanced operational performance. Handle data like never before; perfect results to meet all the business needs. Access features and data that is a class apart by in the industry.
Seamlessly operation by leveraging data, data ready to be explored, visualized and presented in easily digestible dashboards and reports. A single API for all your business data requirements, no need for multiple log-in for accessing quality data. The efficient and dynamic approach to reporting with more actionable insights for marketing and sales performance.
The BigQuery API by dJAX allows a consumer to read disjoint sets of rows from a table using multiple streams within a session. This opens up options from the distributed processing framework or from independent consumer threads within a single client
Reduce tail latency by assigning data to streams dynamically. Remove the business need for complex load balancing with the API and if a stream inside a session didn’t receive a request to read the rows, there is no need for data to be assigned to the stream.
With the assistance of API offered by dJAX, at session creation, users can select an optional subset of columns to read. This allows efficient reads when tables contain many columns.
BigQuery API allows options for users to provide simple filter predicates to enable filtration of data on the server-side before transmission to a client. User interface with customized features for accessing quality data.
Storage sessions read based on a snapshot isolation model. All consumers read based on a specific point in time. The default snapshot time is based on the session creation time, but consumers may read data from an earlier snapshot.
Highly scalable, total access granted and cost-effective API for BigQuery, designed to help you make informed decisions quickly, so you can transform your business with ease. Accelerate time-to-value with a fully managed API that is easy to set up and manage and doesn’t require a database administrator. Jump-start data analysis and uncover meaningful insights to stay competitive.
BigQuerry is a database hosted in the cloud. Access every option and integrate data sources with it through an efficient API. Raw data coming in through the API will be hit-level data. For simple marketing Google Analytics interface data is sufficient, the data obtained through GA is session-based and is aggregated. The Google Analytics interface is relatively easy to use and has a number of tools to make it easy to perform on-the-fly analysis. In order to keep the interface as fast as possible, there are certain limitations in the ways you can access your data and how much you can customize the interface. This is where BigQuery API really comes into action. You’re using the same underlying data like Google Analytics, but you don’t have the same limitations.
Data from other sources are only a glimpse into the data-driven industry and it is now much help while taking digital marketing, content, and usability decisions. With such a large amount of data, there’s always a question about where it should place. BigQuery allows us to combine our Google Analytics data with third-party data sources, in one of two ways: you can either import data into BigQuery, or you can export data out of BigQuery. The API offered by dJAX makes it easy for you to integrate the data sources, thereby making it effective and efficient in handling data-based operations.
Using BigQuery API, access the advanced options offered and it will be a single source for entire data. For example, imagine that you have a CRM. You can configure your website to pass in the Google Analytics client ID into your CRM when a customer record is created. This identifier is unique to every visitor on your website. Once the client ID is in your CRM, you can export your CRM data into BigQuery through the API. The API is of great use to the data industry because the process of getting data into the system can be modified based on the needs- how often is the data updated, how large is the data, and what internal processes you already have in place.
Exporting data from BigQuery is made simpler with the advanced options available in the API. Send data from the database to any data warehouse that accepts CSV import with this API. For example, we might want to summarize web behaviour for each customer in a platform. We could easily obtain the original source/medium information from BigQuery and export it to the platform. Beyond just the raw data, bringing in external data can help with one of the most valuable uses of BigQuery API, audience generation. In addition to identifying the initial audiences, with BigQuery API form dJAX we can also generate dynamic audiences for remarketing, A/B testing and Google Analytics reporting. There are several ways this is possible.
At dJAX, we love that BigQuery API offered by us can integrate with many other data tools. These data form a virtuous cycle, which can help in better understanding of full customer journey. If the dots are connected, the value of all data is amplified. But perhaps the most virtuous cycle of all is the connection between Google Analytics, Google BigQuery and Google Data Studio. There is no better way to visualize analytics data. If the goal is to create one, and only one, “dashboard of record”, this is it. If combining third-party data with BigQuery, only reap greater rewards when all of that data is visualized in one place. Combine Google Analytics data from BigQuery through dJAX API, with customer-connected PII data and see it all, dynamically, inside Data Studio.
With BigQuery API from dJAX there are no limitations when it comes to dimensions. The GA model is structured so that session-based dimensions (like source/medium) don’t play well when combined with user-level or page-level dimensions and metrics. And there’s a limit to the number of dimensions we can see side-by-side: two dimensions are usually limited in the interface, five in custom reports