Key GCP Services to Know for the Associate Data Practitioner Certification

gcp associate data practitioner key gcp services to know for the associate data practitioner certification Apr 29, 2025

From Data Ingestion to Visualization, Areas to master before the exam.

Modern firms now rely heavily on data analytics to spur innovation and make well-informed decisions. Gaining an understanding of the data analytics lifecycle is crucial for effectively utilizing data on Google Cloud. Using simple examples, let’s explore this lifecycle step by step.

The data lifecycle has four steps.

Data Ingestion: Pulling in the raw data—such as device streaming data, on-premises batch data, app logs, or user events and analytics for mobile apps—is the first step.
Data Storage: Following its retrieval, the data must be kept in an easily accessible and long-lasting manner.
Data Processing and Analysis: In order to prepare the data for analysis, the data is converted from its raw form into actionable information at this stage.
Data Exploration and Visualization: The last step is to transform the analysis’s findings into a format that makes it simple to share and gain insights from them with peers and coworkers.

GCP offers a variety of options to handle your data at every stage. This implies that you can choose a group of services that are customized for your data and workflow.

Data Ingestion

Data ingestion into the cloud is the first step in the process. Consider yourself in charge of an online store. Data regarding the transaction, including the item purchased, the price, and the time, must be ingested into Google Cloud for analysis each time a customer makes a purchase. Real-time ingestion is facilitated by tools like Pub/Sub and Dataflow, which guarantee smooth data entry into the system.

Depending on the data’s quantity, source, and latency, there are several methods for gathering raw data.

App: Usually, a push model is used to gather data from app events, including log files or user events, in which the app makes an API call to deliver the data to storage.
Streaming: A steady stream of brief, asynchronous messages makes up the data.
Batch: A collection of files that are moved to storage in bulk contain a lot of data.

Data Storage:

The structure of data is entirely determined by the sources from which it was gathered and the use cases that followed. Data can take many various forms. Data can be kept in a number of locations or formats for data and analytics tasks.

Google BigQuery is heavily tested on the Exam.

Data need a secure home. Google Cloud provides a range of storage choices to suit diverse requirements.

BigQuery, for example, is a powerful data warehouse that allows you to effectively store and query enormous volumes of structured data. As an alternative, you may use Firestore or Cloud Storage if you’re working with unstructured data, such as customer reviews or photos.

Data Processing and Analysis:

To extract valuable insights, the data must be analyzed once it is in the cloud. Using our e-commerce example once more, you may want to examine consumer purchasing trends. This data is processed with the use of tools like Dataproc and Dataflow, which crunch statistics to find trends and patterns.

After that, it’s time to draw conclusions. Suppose you wish to know which products are most popular with consumers. Transactional data may be analyzed using SQL queries in BigQuery to determine customer preferences, peak purchase periods, and best-selling products.

Cracking the GCP Data Practitioner Certification Exam

Data must be transformed and analyzed in order to yield insights and economic value. This calls for tools to evaluate and comprehend processing outcomes in addition to a processing framework that may either analyze the data directly or prepare it for analysis later on.

Processing: Analytical systems retain the data after it has been cleaned, standardized, and processed on several machines.
Analysis: Systems that support ad hoc querying and exploration are used to store processed data.
Understanding: Automated machine-learning models are trained and tested using data based on analytical findings.

Large-scale data processing, big data analysis and query, and machine learning-based data comprehension are all capabilities offered by GCP. Reading data from source systems like Cloud Storage, Cloud Bigtable, or Cloud SQL, and then performing intricate normalizations or aggregations of that data are common steps in large-scale data processing. Frameworks are used to manage distributed compute clusters and provide software tools that facilitate processing because data is frequently too big to fit on a single machine.

Data Exploration and Visualization:

Visuals add interest to a tale that is told by numbers. In addition to helping with data comprehension, visualizations make it easier to effectively communicate findings to stakeholders.

To explain the outcomes of the data processing to a wider audience, you may find it helpful to create intricate data visualizations, dashboards, and reports throughout the analysis phase. GCP interfaces with several dashboarding and reporting solutions to facilitate this process.

You may use Google Looker Studio’s (Data Studio) drag-and-drop report builder to turn data into dashboards and reports that you can share with others. Live data that can be shared and updated supports the reports’ charts and graphs. Interactive controls that let collaborators change the dimensions used to create visualizations can be included in reports.

Google Cloud Looker Studio (Formerly Data Studio)

You can use Google Sheets, Cloud SQL, BigQuery, and existing data files to generate reports and dashboards using Data Studio. You can use BigQuery’s whole processing and storage power by integrating Data Studio with it, eliminating the need to manually import data into Data Studio or build custom connectors.

Google Sheets, which has a direct integration with BigQuery, is an option if you would rather see data in a spreadsheet. BigQuery data and searches may be directly embedded into Google Sheets using Google Apps Script. The results of BigQuery queries can also be exported as CSV files, which you can then open in spreadsheet programs like Google Sheets. Smaller datasets can be produced in this way for analysis or distribution.

Conversely, you may use BigQuery to query across dispersed data sets kept in Google Sheets or Google Drive files.

Gaining an understanding of Google Cloud’s data analytics lifecycle enables analysts to fully utilize data and derive insightful information that propels innovation and well-informed decision-making in today’s data-driven environment.

Make sure you have a firm understanding of these ideas when you study for the GCP Associate Data Practitioner test. You’ll be prepared to answer test questions and use what you’ve learned to solve data problems in the real world.

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