Showing posts with label Google. Show all posts
Showing posts with label Google. Show all posts

April 28, 2022

Top 20 Apache Drill Interview Questions and Answers


        Apache Drill is an open source software framework that enables the interactive study of huge datasets using data demanding distributed applications. Drill is the open source version of Google's Dremel technology, which is provided as a Google Big Query infrastructure service. HBase, MongoDB, MapR-DB, HDFS, MopEDS, AmazonS3, Google cloud storage, Swift, NAS, and local files are among the NoSQL databases and filesystems it supports. Data from various datastores can be combined in a single query. You may combine a user profile collection in MongoDB with a directory of Hadoop event logs, for example.

Apache Kafka Interview Questions and Answers

Ques. 1): What is Apache Drill, and how does it work?


Apache Drill is an open-source SQL engine with no schema that is used to process massive data sets and semi-structured data created by new age Big data applications. Drill's plug-and-play interface with Hive and Hbase installations is a great feature. Google's Dremel file system inspired the Apache Drill. We may have a faster understanding of data analysis without having to worry about schema construction, loading, or any other type of maintenance that used to be required in the RDBMS system. We can easily examine multi-structured data with Drill.

Apache Drill is a schema-free SQL Query Engine for Hadoop, NoSQL, and Cloud Storage that allows us to explore, visualise, and query various datasets without needing to use ETL or other methods to fix them to a schema.

Apache Drill can also directly analyse multi-structured and nested data in non-relational data stores, without any data restrictions.

The schema-free JSON model is included in Apache Drill, the first distributed SQL query engine and its looks like -

  • Elastic Search
  • MongoDB
  • NoSQL database

The Apache Drill is very useful for those professionals that already working with SQL databases and BI tools like Pentaho, Tableau, and Qlikview.

Also Apache Drill supports to -

  • RESTful,
  • ANSI SQL and
  • JDBC/ODBC drivers

Apache Camel Interview Questions and Answers

Ques. 2): Is Drill a Good Replacement for Hive?


Hive is a batch processing framework that is best suited for processes that take a long time to complete. Drill outperforms Hive when it comes to data exploration and business intelligence.

Drill is also not exclusive to Hadoop. It can, for example, query NoSQL databases (such as MongoDB and HBase) and cloud storage (eg, Amazon S3, Google Cloud Storage, Azure Blob Storage, Swift).

Both Instruments Hive and Drill are used to query enormous datasets; Hive is best for batch processing for long-running processes, whereas Drill offers more advancement and a better user experience. Drill's limitation isn't limited to Hadoop; it may also access and process data from other sources.

Apache Struts 2 Interview Questions and Answers

Ques. 3): What are the differences between Apache Drill and Druid?


The primary distinction is that Druid pre-aggregates metrics to give low latency queries and minimal storage use.

You can't save information about individual events while using Druid to analyse event data.

Drill is a generic abstraction for a variety of NoSql data stores. Because the values in these data stores are not pre-aggregated and are saved individually, they can be used for purposes other than storing aggregated metrics.

Drill does not provide the low latency queries required to create dynamic reporting dashboards.

Apache Spark Interview Questions and Answers

Ques. 4): What does Tajo have in common with Apache Drill?


Tajo resembles Drill in appearance. They do, however, have a lot of differences. Their origins and eventual purposes are the most significant contrasts. Drill is based on Google's Dremel, whereas Tajo is based on the combination of MR and parallel RDBMS. Tajo's goal is a relational and distributed data warehousing system, whereas Drill's goal is a distributed system for interactive analysis of large-scale datasets.

As far as I'm aware, the first Drill contains the following characteristics:

  • Drill is a Google Dremel clone project.
  • Its primary goal is to do aggregate queries using a full table scan.
  • Its main goal is to handle queries quickly.
  • It employs a hierarchical data model.

Tajo, on the other hand, has the following features:

  • Tajo combines the benefits of MapReduce and Parallel databases.
  • It primarily targets complex data warehouse queries and has its own distributed query evaluation approach.
  • Its major goal is scalable processing by exploiting the advantages of MapReduce and Parallel databases.
  • We expect that sophisticated query optimization techniques, intermediate data streaming, and online aggregation will significantly reduce query response time.
  • It utilizes a relational data model. We feel that the relational data model is sufficient for modelling the vast majority of real-world applications.
  • Tajo is expected to be linked with existing BI and OLAP software.

Apache Hive Interview Questions and Answers

Ques. 5): What are the benefits of using Apache Drill?


Some of the most compelling reasons to use Apache Drill are listed below.

  • Simply untar the Apache Drill and use it in local mode to get started. It does not necessitate the installation of infrastructure or the design of a schema.
  • Running SQL queries does not necessitate the use of a schema.
  • We can query semi-structured and complex data in real time with Drill.
  • The SQL:2003 syntax standard is supported by Apache Drill.
  • Drill can be readily linked with BI products like QlikView, Tableau, and MicroStrategy to give analytical capabilities.
  • We can use Drill to conduct an interactive query that will access the Hive and HBase tables.
  • Drill supports multiple data stores such as local file systems, distributed file systems, Hadoop HDFS, Amazon S3, Hive tables, HBase tables, and so on.
  • Apache Drill can be easily scalable from a single system up to 1000 nodes.

Apache Tomcat Interview Questions and Answers 

Ques. 6): What Are the Great Features of Apache Drill?


The following features are -

  • Schema-free JSON document model similar to MongoDB and Elastic search
  • Code reusability
  • Easy to use and developer friendly
  • High performance Java based API
  • Memory management system
  • Industry-standard API like ANSI SQL, ODBC/JDBC, RESTful APIs
  • How does Drill achieve performance?
  • Distributed query optimization and execution
  • Columnar Execution
  • Optimistic Execution
  • Pipelined Execution
  • Runtime compilation and code generation
  • Vectorization

Apache Ambari interview Questions and Answers

Ques. 7): What are some of the things we can do with the Apache Web interface?


The tasks that we can conduct through the Apache Drill Web interface are listed below.

  • The SQL Queries can be conducted from the Query tab.
  • We have the ability to stop and restart running queries.
  • We can view the executed queries by looking at the query profile.
  • In the storage tab, you can view the storage plugins.
  • In the log tab, we can see logs and stats.

Apache Tapestry Interview Questions and Answers

Ques. 8): What is Apache Drill's performance like? Does the number of lines in a query result affect its performance?


We utilise drill for its rest server and connect D3 visualisation for querying IOT data, and the querying command(select and join) suffers from a lot of slowness, however this was fixed when we switched to spark SQL.

Drill is useful in that it can query most data sources, but it may need to be tested before being used in production. (If you want something faster, I believe you can find a better query engine.) But for development and testing, it's been quite useful.

Apache Ant Interview Questions and Answers

Ques. 9): What Data Storage Plugins does Apache Drill support?


The following is a list of Data Storage Plugins that Apache Drill supports.

  • File System Data Source Storage Plugin
  • HBase Data Source Storage Plugin
  • Hive Data Source Storage Plugin
  • MongoDB Data Source Storage Plugin
  • RDBMS Data Source Storage Plugin
  • Amazon S3 Data Source Storage Plugin
  • Kafka Data Source Storage Plugin
  • Azure Blob Data Source Storage Plugin
  • HTTP Data Source Storage Plugin
  • Elastic Search Data Source Storage Plugin

Apache Cassandra Interview Questions and Answers

Ques. 10): What's the difference between Apache Solr and Apache Drill, and how do you use them?


The distinction between Apache Solr and Apache Drill is comparable to that between a spoon and a knife. In other words, despite the fact that they deal with comparable issues, they are fundamentally different instruments.

To put it plainly... Apache Solr is a search platform, while Apache Drill is a platform for interactive data analysis (not restricted to just Hadoop). Before performing searches with Solr, you must parse and index the data into the system. For Drill, the data is stored in its raw form (i.e., unprocessed) on a distributed system (e.g., Hadoop), and the Drill application instances (i.e., drillbits) will process it in parallel.

Apache NiFi Interview Questions and Answers

Ques. 11): What is the recommended performance tuning approach for Apache Drill?


To tune Apache Drill's performance, a user must first understand the data, query plan, and data source. Once these locations have been discovered, the user can utilise the performance tuning technique below to increase the query's performance.

  • Change the query planning options if necessary.
  • Change the broadcast join options as needed.
  • Switch the aggregate between one and two phases.
  • The hash-based memory-constrained operators can be enabled or disabled.
  • We can activate query queuing based on your needs.
  • Take command of the parallelization.
  • Use partitions to organise your data.

Apache Storm Interview Questions and Answers

Ques. 12): What should you do if an Apache Drill query takes a long time to deliver a result?


Check the following points if a query from Apache Drill is taking too long to deliver a result.

  • Check the query's profile to determine if it's moving or not. The query progress is determined by the time of the latest update and change.
  • Streamline the process where Apache Drill is taking too long.
  • Look for partition pruning and projection pushdown operations.


Ques. 13): I'm using Apache Drill with one drillbit to query approximately 20 GB of data, and each query takes several minutes to complete. Is this normal?


The performance of a single bit drill is determined by the Java memory setup and resources available on the computer where your query is being performed. Because the query engine must identify meaningful matches, the where clause requires more work from the query engine, which is why it is slower.

You can also alter JVM parameters in the drill configuration. You can devote more resources to your searches, which should result in speedier results.


Ques. 14): How does Apache Drill compare to Apache Phoenix with Hbase in terms of performance?


Because Drill is a distributed query engine, this is a fascinating question. In contrast, Phoenix implements RDBMS semantics in order to compete with other RDBMS. That isn't to suggest that Drill won't support inserts and other features... But, because they don't do the same thing right now, comparing their performance isn't really apples-to-apples.

Drill can query HBase and even push query parameters down into the database. Additionally, there is presently a branch of Drill that can query data stored in Phoenix.

Drill can simultaneously query numerous data sources. Logically if you choose to use Phoenix, you could use both to satisfy your business needs.


Ques. 15): Is Apache Drill 1.5 ready for usage in production?


Drill is one of the most mature SQL-on-Hadoop solutions in general. As with all of the SQL-on-Hadoop solutions, it may or may not be the best fit for your use case. I mention that solely because I've heard of some extremely far-fetched use cases for Drill that aren't a good fit.

Drill will serve you well in your production environment if you wish to run SQL queries without "requiring" ETL first.

Any tool that supports the ODBC and JDBC connections can easily access it as well.


Ques. 16): Why doesn't Apache Drill get the same amount of attention as other SQL-on-Hadoop tools?


To keep track of SQL on Hadoop tools and to advise enterprise customers on which ones would be ideal for them. A lot of SQL on Hadoop solutions have a large number of users. Presto has been used by a number of major Internet firms (Netflix, AirBnB), as well as a number of large corporations. It is largely sponsored by Facebook and Teradata (my job). The Cloudera distribution makes Impala widely available. Phoenix and Kylin also make a lot of appearances and have a lot of popularity. Until it doesn't function or a flaw is discovered, Spark SQL is the go-to for new projects these days. Hive is the hard to beat incumbent. Adoption is crucial.


Ques. 17): Is it possible to utilise Apache Drill + MongoDB in the same way that RDBMS is used?


To begin, you must comprehend the significance of NoSQL. To be honest, deciding between NoSQL and RDBMS based on a million or ten million users is not a great number.

However, as you stated, the size of your dataset will only grow. You can begin using MongoDB, keeping in mind the scalability element.

Apache Drill is now available.

Dremel by Google was the inspiration for Apache drill. When you select columns to retrieve, it performs well. Multiple data sources can be joined together (e.g. join over hive and MongoDB, join over RDBMS and MongoDB, etc.)

Also, pure MongoDB or MongoDB + Apache Drill are both viable options.


Stick to native MongoDB if your application architecture is entirely based on MongoDB. You have access to all of MongoDB's features. MongoDB java driver, python driver, REST API, and other options are available. Yes, learning MongoDB-specific concepts will take more time. However, RDBMS queries provide you a lot of flexibility, and you can do a lot of things over here.

MongoDB + Apache Drill

You can choose this option if you can accomplish your goal with JPA or SQL queries and you are more familiar with RDBMS queries.

Additional benefit: You can use dig to query across additional data sources such as hive/HDFS or RDBMS in addition to MongoDB in the future.


Ques. 18): What is an example of a real-time use of Apache Drill? What makes Drill superior to Hive?


Hive is a batch processing framework that is best suited for processes that take a long time to complete. Drill outperforms Hive when it comes to data exploration and business intelligence.

Drill is also not exclusive to Hadoop. It can, for example, query NoSQL databases (such as MongoDB and HBase) and cloud storage (eg, Amazon S3, Google Cloud Storage, Azure Blob Storage, Swift).


Ques. 19): Is Cloudera Impala similar to the Apache Drill incubator project?


It's difficult to make a fair comparison because both initiatives are still in the early stages. We still have a lot of work to do because the Apache Drill project was only started a few months ago. That said, I believe it is critical to discuss some of the Apache Drill project's techniques and goals, which are critical to comprehend when comparing the two:

  • Apache Drill is a community-driven product run under the Apache foundation, with all the benefits and guarantees it entails.
  • Apache Drill committers are scattered across many different companies.

Apache Drill is a NoHadoop (not just Hadoop) project with the goal of providing distributed query capabilities across a variety of large data systems, including MongoDB, Cassandra, Riak, and Splunk.

  • By supporting all major Hadoop distributions, including Apache, Hortonworks, Cloudera, and MapR, Apache Drill avoids vendor lock-in.
  • Apache Drill allows you to do queries on hierarchical data.
  • JSON and other schemaless data are supported by Apache Drill.
  • The Apache Drill architecture is built to make third-party and custom integrations as simple as possible by clearly specifying interfaces for query languages, query optimizers, storage engines, user-defined functions, user-defined nested data functions, and so on.

Clearly, the Apache Drill project has a lot to offer and a lot of qualities. These things are only achievable because of the enormous amount of effort and interest that a big number of firms have begun to contribute to the project, which is only possible because of the Apache umbrella's power.


Ques. 20): Why is MapR mentioning Apache Drill so much?


Originally Answered: Why is MapR mentioning Apache Drill so much?

Drill is a new and interesting low latency SQL-on-Hadoop solution with more functionality than the other options available, and MapR has done it in the Apache Foundation so that it, like Hive, is a real community shared open source project, which means it's more likely to gain wider adoption.

Drill is MapR's baby, so they're right to be proud of it - it's the most exciting thing to happen to SQL-on-Hadoop in years. They're also discussing it since it addresses real-world problems and advances the field.

Consider Drill to be what Impala could have been if it had more functionality and was part of the Apache Foundation.




April 15, 2022

Top 20 Google Cloud Computing Interview Questions and Answers

The Google Cloud Computing Platform is a rapidly evolving industry standard, and many organizations have a successful application that is promoted in a variety of ways. Every organization has a variety of cloud computing options, including roles such as Cloud Computing Manager, Cloud Computing Architect, Module Lead, Cloud Engineer, Cloud Computing Trainer, and so on. Below are the most often asked questions and answers in this sector, which will be useful to all candidates.

Google Cloud Platform (GCP) is a set of cloud computing services supplied by Google that run on the same infrastructure as Google's internal products, such as Google Search, Gmail, and YouTube.

Google has introduced a number of cloud services to the App Engine platform since its launch. Its specialization is offering a platform for individuals and businesses to create and execute software, and it connects those users over the internet.


Ques. 1): What do you understand by Cloud Computing?


Cloud computing is described as computer power that is entirely stored in the cloud at all times. It is one of the most recent developments in the online saga sector, and it mostly relies on the Internet, i.e. the Cloud, for delivery. The cloud computing service is genuinely worldwide, with no regional or border limits.


Ques. 2): What is the difference between cloud computing and virtualization?


        Cloud computing is a set of layers that work together to provide IP-based computing; virtualization is a layer/module inside the cloud computing architecture that allows providers to supply IaaS (Infrastructure as a Service) on demand.

        Virtualization is a software that allows you to generate "isolated" images of your hardware and software on the same machine. This allows various operating systems, software, and applications to be installed on the same physical computer.


Ques. 3): Tell us about Google Cloud's multiple tiers.


The Google cloud platform is divided into four layers:

1. Infrastructure as a Service (IaaS): This is the foundational layer, which includes hardware and networking.

2. Platform as a Service (PaaS): This is the second layer, which includes both the infrastructure and the resources needed to construct apps.

3. Software as a Service (SaaS): SaaS is the third layer that allows users to access the service provider's numerous cloud products.

4. Business Process Outsourcing: Despite the fact that BPO is not a technical solution, it is the final layer. BPO refers to outsourcing services to a vendor who would handle any issues that the end-user may encounter when using cloud computing services.


Ques. 4): What are the most important characteristics of cloud services?


Cloud computing and cloud services as a whole provide a slew of capabilities and benefits. The items listed below are the same. The convenience of being able to access and control commercial software from anywhere on the planet.

        The capacity to build and develop web applications capable of handling multiple customers from around the world at the same time, and to quickly centralise all software management tasks to a central web service.

        By centralising and automating the updating process for all applications installed on the platform, the need to download software upgrades will be eliminated.


Ques. 5): What is GCP Object Versioning?


Object versioning is a method of recovering data that has been overwritten or destroyed. When objects are destroyed or overwritten, object versioning increases storage costs while maintaining object security. When you activate object versioning in your GCP bucket, a noncurrent version of the object is created every time the item is overwritten or removed. To identify a variant of an entity, properties generation and meta generation are utilised. The phrase generation refers to the act of producing material, whereas meta generation is the process of producing metadata.


Ques. 6): Why is it necessary for businesses to manage their workload?


A workload in an organisation can be characterised as a self-contained service with its own set of code that must be executed. Everything from data-intensive workloads to transaction and storage processing is included in this task. All of this labour is independent of external factors.

The following are the primary reasons why businesses should manage their workload.

        To get a sense of how their applications are performing.

        To be able to pinpoint exactly what functions are taking place.

        To obtain a sense of how much a specific agency will charge for using these services.


Ques. 7): What is the relationship between Google Compute Engine and Google App Engine?


        Google Compute Engine and Google App Engine are mutually beneficial. Google Compute Engine is an IaaS service, while Google App Engine is a PaaS service.

        Web-based applications, mobile backends, and line-of-business applications are typically operated on Google App Engine. Compute Engine is an excellent alternative if you want more control over the underlying infrastructure. Compute Engine, for example, can be used to construct bespoke business logic or to run your own storage system.


Ques. 8): What are the main components of the Google Cloud Platform?


The Google Cloud Platform (GCP) is made up of a number of components that assist users in various ways. I'm familiar with the following GCP elements:

                    Google Compute Engine

                    Google Cloud Container Engine

                    Google Cloud App Engine

                    Google Cloud Storage

                    Google Cloud Dataflow

                    Google BigQuery Service

                    Google Cloud Job Discovery

                    Google Cloud Endpoints

                    Google Cloud Test Lab

                    Google Cloud Machine Learning Engine


Ques. 9): What are the different GCP roles you can explores?

Within Google Cloud Platform, there are many positions based on the tasks and responsibilities.

        Cloud software engineer: A cloud software engineer is a software developer who focuses on cloud computing systems. This position entails the creation of new systems or the upgrade of current ones.

        Cloud software consultant: This position comprises finding solutions to Google's cloud computing customers' complicated problems.

        Technical programme managers: To oversee the planning, communication, and execution of diverse cloud solutions, you'll require appropriate technical competence in cloud computing.

        Cloud engineering managers: Software engineers hired for this position are responsible for designing and delivering internet-scale solutions and products within the cloud computing infrastructure.

        Cloud engineering support: As a software engineer, you could be in charge of managing cloud computing systems and providing technical help to cloud customers who are having problems.

        Product managers for cloud products: As a product manager, you'd be in charge of overseeing the development of new cloud products from conception to launch.


Ques. 10): In Google Cloud Storage, what is a bucket?


Buckets are the most fundamental containers for storing information. You may arrange data and grant control access to buckets. The bucket has a globally unique name that corresponds to the location where the contents are kept. It also contains a default storage class that is applied to objects that are added to the bucket without a storage class defined. The number of buckets that can be created or deleted is similarly unlimited.


Ques. 11): What is Cloud Armor, exactly?


It will aid in the protection of your infrastructure and application from DDoS attacks. It protects your infrastructure by working with HTTPS load balancers. For the same, we can accept or disallow the rule. Cloud Armor's rules language is flexible, allowing for customization of defence and mitigation of attacks. It also contains predefined rules to protect against application-aware cross-site scripting (XSS) and SQL injection (SQLi) attacks. If you're running a web application, the allow and deny rules you set up will help you protect against SQL injection, DDoS attacks, and other threats.


Ques. 12): In cloud computing, what is load balancing?

Ans: In a cloud computing context, load balancing is the practise of spreading computer resources and workloads to control demand. It aids in achieving high performance at lower costs by effectively managing workload demands through resource allocation. It makes use of the concepts of scalability and agility to increase resource availability in response to demand. It's also utilised to keep track of the cloud application's health. All of the major cloud companies, such as AWS, GCP, Azure, and others, provide this feature.


Ques. 13): What is Google BigQuery, and how does it work? What are the advantages of BigQuery for data warehouse administrators?

Ans: Google BigQuery is a software platform that replaces the traditional data warehouse's hardware architecture. It is employed as a data warehouse and hence serves as a central repository for all of an organization's analytical data. In addition, BigQuery divides the data table into components called as datasets.

For data warehouse practitioners, BigQuery comes in handy in a number of ways. Here are a few of them:

        BigQuery dynamically assigned query resources and storage resources based on demand and usage. As a result, it does not necessitate resource provisioning prior to use.

·         For efficient storage management, BigQuery stores data in a variety of ways, including proprietary format, proprietary columnar format, query access pattern, Google's distributed file system, and others.

        BigQuery is completely up to date and controlled.

        BigQuery enables a broader level of backup recovery and catastrophe recovery.

        BigQuery engineers manage the service's updates and maintenance completely without any downtime or performance degradation. Users can easily reverse changes and return to a previous state without having to request a backup recovery.


Ques. 14): What are the primary benefits of utilising Google Cloud Platform?


Google Cloud Platform is a platform that connects customers to the greatest cloud services and features available. It is gaining popularity among cloud experts and users due to the benefits it provides.

The following are the key benefits of adopting Google Cloud Platform over other platforms:

        When compared to other cloud service providers, GCP offers significantly lower price.

        When it comes to hosting cloud services, GCP has improved performance and service generally.

        Google Cloud is very quick to provide server and security updates in a more timely and effective manner.

        The security level of Google Cloud Platform is exemplary; the cloud platform and networks are secured and encrypted with various security measures.


Ques. 15): What are the different types of service accounts? How are you going to make one?


        The service accounts are used to authorise Google Compute Engine to undertake tasks on behalf of the user, allowing it to access non-sensitive data and information.

        By handling the user's authorization procedure, these accounts often facilitate the authentication process from Google Cloud Engine to other services. It is important to note that service accounts are not utilised to gain access to the user's information.

        Google offers several different sorts of service accounts, however most users prefer to use one of two types of service accounts:

        Service accounts for Google Cloud Platform Console

        Accounts for the Google Compute Engine service

The user doesn’t need to create a service account manually. It is automatically created by the Compute Engine whenever a new instance is created. Google Compute Engine also specifies the scope of the service account for that particular instance when it is created.


Ques. 16): What are the multiple Google Cloud SDK installation options?


The Google Cloud SDK can be installed using one of four distinct methods. The user can install Google Cloud Software Development Kit using any of the options below, depending on their needs.

        Using Google Cloud SDK with scripts, continuous integration, or continuous deployment — in this scenario, the user can download a versioned archive for a non-interactive installation of a given version of Cloud SDK.

        YUM is used to download the latest published version of the Google Cloud SDK in package format when operating Red Hat Enterprise Linux 7/CentOS 7.

        APT-Download is used to get the latest released version of the Google Cloud SDK in package format while operating Ubuntu/Debian.

        The user can utilise the interactive installer to install the newest version of the Google Cloud SDK for all other use cases.


Ques. 17): How are you going to ask for greater quota for your project?


        Default quotas for various types of resources are provided to all Google Compute Engine projects. Quotas can also be increased on a per-project basis.

        If you find that you have hit the quota limit for your resources and wish to increase the quota, you can make a request for more quota for some specific resources using the IAM quotas page on the Google Cloud Platform Console. Using the Edit Quotas button at the top of the page, you can request more quota.


Ques. 18): What are your impressions about Google Compute Engine?


        Google Compute Engine is an IaaS offering that provides self-managed and configurable virtual machines hosted on Google's infrastructure. It features virtual machines based on Windows and Linux that may run on local, KVM, and persistent storage, as well as a REST-based API for control and setup.

        Google Compute Engine interfaces with other Google Cloud Platform technologies, such as Google App Engine, Google Cloud Storage, and Google BigQuery, to expand its computing capabilities and hence enable more sophisticated and complicated applications.


Ques.19): What is the difference between a Project Number and a Project Id?


The two elements that can be utilised to identify a project are the project id and the project number. The distinctions between the two are as follows:

When a new project is created, the project number is generated automatically, whereas the project number is generated by the user. The project number is necessary for many services, however the project id is optional (but it is a must for the Compute Engine).


Ques. 20): What are BigQuery's benefits for data warehouse administrators?


        BigQuery is useful for data warehouse professionals in a variety of ways. Here are several examples:

        BigQuery allocated query and storage resources dynamically based on demand and usage. As a result, resource provisioning is not required prior to use.

        BigQuery stores data in a number of formats for effective storage management, including proprietary format, proprietary columnar format, query access pattern, Google's distributed file system, and others.

        BigQuery is a fully managed and up-to-date service. Without any downtime or performance reduction, BigQuery engineers manage all of the service's updates and maintenance.