Here are the top Google BigQuery interview questions and answers in 2023 to help the candidate do well in the interview. We also provided the most important topics and ideas that could come up in an interview, such as data storage, query optimization, security, and scalability. If you look over all of these well-known BigQuery interview questions, you will definitely do well in your interview.
BigQuery is a google-managed enterprise data warehouse with built-in features like business intelligence, geospatial analysis, and machine learning capabilities that allow you to manage and analyze the data.
MindMajix experts have compiled the most frequently asked BigQuery interview questions and answers to help you in the interview preparation process. These interview questions are classified into three levels: technical, experienced, and scenario-based. They cover many BigQuery-related topics and challenging questions for interview-preparing candidates.
Google BigQuery is a big data analytics web service that runs in the cloud and is designed to process very massive read-only data collections.
BigQuery is a fully managed, serverless data warehouse that enables petabyte-scale data processing. BigQuery's serverless architecture allows you to perform SQL queries to resolve your business's most pressing issues. Using BigQuery's distributed analytical engine, you may query terabytes in seconds and petabytes in minutes.
Google BigQuery architecture consists of the majority of 4 parts. They are
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Some of the GCP BigQuery benefits include
Bigquery Query Cache is what makes the platform's data retrieval so swift.
BigQuery uses a temporary cached results table to save query results during the first execution. We call this "Query Cache" for short.
The following are the 12 components that make up Google BigQuery:
The application known as BigQuery Data Transfer Service is the tool that should be utilized for the most successful loading of data into BigQuery. With the assistance of this tool, you will be able to swiftly and efficiently import data into BigQuery from various sources, including other services offered by the Google Cloud Platform.
Data can be represented in BigQuery Storage using rows, columns, and tables, and the columnar Storage format, which is optimized for analytical queries, can be used to store the data.
BigQuery Storage also assists with storing the data. It supplies comprehensive assistance for database transaction semantics (ACID). It is possible to replicate it across many sites to provide high availability.
Sharding is the process of breaking data into smaller pieces so that it can be handled and managed more quickly and easily. When working with BigQuery, sharding, which is the process of dividing the data across multiple processors, can be used to speed things up overall.
The following is a list of various approaches to optimize the computation of queries in Big Query:
The performance of queries can be helped in BigQuery by partitioning tables. This enables the query engine to cut down the amount of data it needs to scan, improving query performance.
For instance, if you have a table with data for many different years, you may divide the database into partitions based on the years. Then, if you run a query that needs data from a particular year, the query engine can skip over the other partitions, saving you both time and resources.
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Using the following command, we may convert a stringified array to an array from a BigQuery Table:
COMMAND: #standardSQL WITH k AS ( SELECT 1 AS id, '["a", "b", "c"]' AS x UNION ALL SELECT 2, '["x", "y"]' ) SELECT id, ARRAY(SELECT * FROM UNNEST(SPLIT(SUBSTR(x, 2 , LENGTH(x) - 2)))) AS x FROM k
Using the following command, we may determine the BigQuery storage size for a single table:
COMMAND: select sum(size_bytes)/pow(10,9) as size from <your_dataset>.__TABLES__ where table_id = '<your_table>'
Using the following command, we may handle Google API Errors with Python from a BigQuery Table:
COMMAND: from googleapiclient.errors import HttpError try: ... except HttpError as err: # If the error is a rate limit or connection error, # wait and try again. if err.resp.status in [403, 500, 503]: time.sleep(5) else: raise
Using the following command, we may fetch each user between two dates from a BigQuery Table:
COMMAND: SELECT timestamp_trunc(timestamp, DAY) as Day, user_id, count(1) as Number FROM `table` WHERE timestamp >= '2023-12-28 00:00:00 UTC' AND timestamp <= '2023-12-27 23:59:59 UTC' GROUP BY 1, 2 ORDER BY Day
Using the following command, we may delete duplicate rows from a BigQuery Table:
COMMAND: SELECT * FROM ( SELECT *, ROW_NUMBER() OVER (partition by Fixed Accident Index) row_number FROM Accidents.Cleaned FilledCombined ) WHERE row_number = 5
Using the following command, we may create a temporary table from a BigQuery:
COMMAND: SELECT * INTO <TEMP TABLE> FROM <name>
Google Cloud Storage is utilized as an intermediary storage layer to import data into BigQuery because of the reasonable pricing of the cloud data storage that it provides. You can significantly reduce the high expenses associated with cloud storage if you use Google Cloud Storage rather than one of the many other cloud storage providers.
Once BigQuery has been configured, it can be accessed in several ways.
Encrypting data before it is stored in BigQuery is the most effective technique to ensure that a company complies with the standards set forth by the GDPR.
Since BigQuery offers a variety of encryption strategies, you are free to choose and choose the one that works best for your organization. Consider implementing a data access control system to ensure that only individuals with a legitimate need for the information can access it.
You are given the ability to develop views with Google BigQuery. You can use the command line interface (CLI), the BigQuery online UI, or the API to accomplish this. Before developing a view, you will need first to make a dataset. You can generate a view after that dataset has been created.
Google BigQuery supports multiple input formats when receiving data.
BigQuery's web-based user interface is another option for transferring data files. In addition to importing data from a local file or a Google Cloud Storage bucket, the BigQuery command-line tool can do the same for a Google Cloud Datastore bucket. BigQuery's application programming interface (API) then lets you import records from numerous sources.
Google BigQuery is a cloud-based architecture that offers remarkable performance due to its ability to auto-scale up and down depending on the amount of data load and rapidly perform data analysis.
On the other hand, SQL Server employs a client-server architecture and, unless the user scales it manually, maintains a constant level of performance throughout the system.
A database that organizes its data into columns instead of to rws is known as a columnar database. BigQuery is a columnar database that contains data in columns rather than rows, like traditional relational databases (RDMS) do. Because of this, it is an excellent choice for storing massive volumes of data and conducting queries on that data.
Below are some of the Google BigQuery features, including
Check that your Query follows the proper syntax using the Query Validator. If you try to run a query that already has errors, the attempt will fail, and the error will be logged in the Job details. The query validator will show a tick in the green box whenever there are no problems with the Query. Click the Run button to execute the Query and see the results after the checkmark appears in the green box.
Using standard SQL to query data in BigQuery is the most up-to-date and recommended way. The SQL:2011 standard, on which it is based, provides numerous enhancements over older versions of the language. Performance enhancements, more assistance for SQL standard features, and enhanced compatibility with other SQL-based systems are only some of how this has been enhanced.
Legacy SQL is a way of querying data in BigQuery that predates the SQL:2003 standard. While traditional SQL is still supported for compatibility reasons, it is strongly recommended that you use more modern forms of the language whenever possible.
The following reports are examples of those that can be generated from the data contained in BigQuery:
Google Cloud Platform App Engine provides and ensures our application's capacity and availability. It is done by supplying the capability.
It is responsible for offering built-in services and APIs, managing the servers and infrastructures on our behalf, and controlling the traffic to our websites.
When it comes to the construction of our application, GCP App Engine offers interaction with a variety of development tools, like GIT, Eclipse, Jenkins, and Maven, among others, so that our workflow is not disrupted.
Functions that aggregate to a single value for a set of rows are called "window functions," They're helpful for computing values over a set of rows and returning just that result.
It has three different kinds of functions, such as:
The following are methods for connecting to the BigQuery Cloud Data Warehouse:
Using time decorators in Big Query enables access to historical data. For instance, if you accidentally deleted a table an hour ago, you may still retrieve the data from the table using a time decorator.
BigQuery was explicitly developed to manage massive data collections. It can store up to 10 petabytes of data and can analyze up to 100 terabytes of data every single day.
The procedure of installing and configuring BigQuery is simple enough. After establishing a project in the Google Cloud Console, you can activate the BigQuery API. Creating a project is the first step. After enabling the API, you can begin creating datasets and running queries.
Here are some ways that Big Query can be used with window functions:
Big Query's conversion methods allow for the explicit transformation of data types. The following syntax can be used to convert an expression to a string:
CAST (expr AS STRING)
In BigQuery, the size of a table you build is not limited by a maximum value.
The answer is yes; you can publicize your searches and data on Google BigQuery. You can implement this by launching a project, discussing it with specific people, or making it available to the general public.
Google's BigQuery is a fully managed serverless data warehouse that supports scalable analysis of data sets up to several petabytes in size. A cloud computing environment that supports ANSI SQL queries.
BigQuery is a solution for OLAP, which stands for online analytical processing.
BigQuery is best suited for large workloads, such as regular OLAP reporting and archiving activities. It is because query latency is significant in BigQuery. BigQuery's architecture discourages OLTP-style queries.
Yes, BigQuery and Snowflake are two of the best ETL software solutions for businesses that want to manage their data from various sources and get the most out of their data insights. These businesses aim to gain as much as they can from their data.
The query engine can process petabyte-scale data using standard SQL queries in seconds, and terabyte-scale data takes only minutes. BigQuery delivers tremendous efficiency with no requirement for index creation, infrastructure maintenance, or rebuilding. Because of its speed and scalability, BigQuery is well-suited for processing massive datasets.
Both the Google Standact and legacy Sdialectsect are available for use with BigQuery.
BigQuery supports the Google Standard SQL dialect. If you are unfamiliar with BigQuery, you should start with Google Standard SQL because it has the most comprehensive features. For instance, features such as DDL and DML statements are only supported when using Google Standard SQL.
When creating an empty table or loading data into an existing one, BigQuery allows you to choose the table's schema. On the other hand, you can utilize schema auto-detection to find out what file formats can be used to store your data.
BigQuery's data storage is a fully managed service. No need to set aside storage space or reserve a certain amount of storage capacity. When you upload data to BigQuery, the service immediately begins allocating storage space. Only the space you actually use will be charged to you.
BigQuery is intended for typical SQL queries on structured and semi-structured data. It is extremely cost-effective and highly optimized for query performance. BigQuery is a fully managed cloud service; therefore, there is no operational overhead.
Yes, A data lake, notably the popular and convenient Google BigQuery, is the greatest solution to store data. The data lake on Google Cloud drives any study on any sort of data.
BigQuery is an entirely managed enterprise data warehouse that offers built-in technologies like geospatial analysis, machine learning, and business intelligence to assist you in analyzing and managing your data.
Depending on the size and complexity of each query, BigQuery determines how many slots are needed. At each level of the query, separate units of work are carried out by BigQuery slots. For a certain step of a query, BigQuery can ask for an unlimited number of slots.
For example, If BigQuery finds that the best parallelization factor for a stage is 10, it asks 10 slots to process that stage.
We've covered all the Google BigQuery interview questions and answers that will likely ask during the interview. If you are thinking of preparing for a BigQuery interview, check out all these important questions to ace the interview well. To learn more, have a look at our Google SQL Server Training.
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Madhuri is a Senior Content Creator at MindMajix. She has written about a range of different topics on various technologies, which include, Splunk, Tensorflow, Selenium, and CEH. She spends most of her time researching on technology, and startups. Connect with her via LinkedIn and Twitter .
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