Data engineers are responsible for managing unstructured data as well as emerging data types such as streaming data. It necessitates learning and understanding of a new set of tools, procedures, platforms, extra technologies like HDInsight and Cosmos DB, and programming languages like Hive and Python. Furthermore, being a Data Engineer necessitates acing a job interview. As a result, we at Testpreptraining have put together this post with the most commonly asked interview questions and their solutions for your convenience.
1. What is your experience with SQL Server?
SQL Server is a relational database management system (RDBMS) developed by Microsoft. It is used to store and manage data for a wide range of applications, including data warehousing, business intelligence, and web-based applications. SQL Server supports a variety of data types and includes features such as indexing, transactions, and stored procedures. It also offers robust security features, including encryption and auditing, and can be run on-premises or in the cloud using Microsoft Azure.
2. Explain your understanding of data warehousing concepts.
Data warehousing is the process of collecting, storing, and managing large amounts of data from various sources for reporting and analysis. Key concepts in data warehousing include:
- Data integration: Combining data from multiple sources into a single, unified data store.
- Data cleaning: Removing duplicates, correcting errors, and transforming data into a consistent format.
- Data warehousing schema: A logical structure for organizing data in a data warehouse, such as a star schema or snowflake schema.
- Data mart: A subset of a data warehouse that is designed to serve a specific department or business unit.
- Data extraction, transformation, and loading (ETL): The process of extracting data from source systems, transforming it into a format suitable for the data warehouse, and loading it into the data warehouse.
- Data aggregation: The process of summarizing data in a data warehouse to improve query performance and reduce data storage requirements.
- Data analysis: The process of using data in a data warehouse to gain insights, make decisions, and support business processes.
3. Can you walk me through your experience with designing and building large-scale data processing systems?
Designing and building large-scale data processing systems involves the following steps:
- Understanding the business requirements and defining the data processing objectives.
- Analyzing the data sources and determining the data architecture to meet the processing requirements.
- Designing the data storage systems to handle the data volume and velocity, such as data warehousing, NoSQL databases, or cloud storage solutions.
- Implementing data ingestion pipelines to transfer data from the sources to the storage systems.
- Creating data processing workflows using tools such as Apache Spark, Apache Beam, or Azure Data Factory.
- Designing and implementing data quality checks to ensure the accuracy and completeness of the data.
- Monitoring the system performance and scalability, and making changes as necessary to ensure reliability.
- Implementing security and privacy controls to protect sensitive data.
- Collaborating with data scientists and data analysts to integrate the processed data into their workflows.
- Continuously testing and improving the system to meet changing business requirements and evolving technology trends.
4. Why did you prefer a profession in Data Engineering?
An interviewer will probably ask this question to study more regarding your motivation and enthusiasm in preferring data engineering as a profession. They want to hire individuals who are passionate and excited about the field. The candidate can begin by sharing their story and insights they have obtained to highlight what excites them the most about being a certified data engineer.
5. What is your experience with Azure services such as Azure Data Factory, Azure Stream Analytics, and Azure HDInsight?
Azure Data Factory: Azure Data Factory is a cloud-based data integration service that enables users to create, schedule, and orchestrate data pipelines. It can transfer data between on-premises and cloud data stores, and perform data transformations using Azure Machine Learning and Azure HDInsight.
Azure Stream Analytics: Azure Stream Analytics is a real-time data stream processing service that enables users to analyze and process fast-moving data from a variety of sources, such as IoT devices, social media, and logs.
Azure HDInsight: Azure HDInsight is a cloud-based service for big data processing using Apache Hadoop and Spark. It provides a managed environment for running big data workloads, making it easier for users to analyze large data sets and extract insights from them.
These services can be used together to create end-to-end big data processing solutions on the Azure platform. For example, Azure Data Factory can be used to transfer data from sources to Azure HDInsight for processing, and the processed data can then be streamed to Azure Stream Analytics for real-time analysis.
6. How have you tackled data migration from on-premises to cloud environments?
Data migration from on-premises to cloud environments involves the following steps:
Assessing the data: Evaluate the existing data sources, data volumes, and data formats to determine what needs to be migrated to the cloud.
Planning the migration: Decide on the migration strategy, including the data transfer method, data mapping, and timeline.
Preparing the data: Cleanse and transform the data to ensure its compatibility with the cloud environment.
Transferring the data: Use tools such as Azure Data Factory, AWS Data Pipeline, or Google Cloud Data Transfer to transfer the data from on-premises to the cloud.
Validating the data: Verify the accuracy and completeness of the data after migration, and resolve any discrepancies.
Updating applications and systems: Update any systems and applications that rely on the migrated data to point to the new cloud location.
Monitoring and maintaining: Continuously monitor the performance of the cloud-based data processing systems, and make changes as necessary to ensure reliability and performance.
Data migration to the cloud can bring many benefits, such as increased scalability, reduced maintenance costs, and improved disaster recovery. However, it is important to consider the risks and costs involved and to plan and execute the migration carefully to minimize downtime and ensure data consistency and integrity.
7. Why do we require Azure Data Factory?
- The value of data produced these days is large and this data comes from various sources. When we pass this precise data to the cloud, there are few elements that should be taken into consideration.
- Data can be in any pattern as it arrives from various sources and these sources will transport or channelize the data in several ways and it can be in a distinctive format. When we take this data to the cloud or selective storage we require to make certain that this data is well maintained. i.e you require to modify the data, and remove undesirable parts. As per stimulating the data is involved, we need to make certain that data is pulled from various sources and take it at one general place then store it and if needed we should convert it into more significant.
- Data factory benefits to compose this complete method in a more organizable or manageable manner.
8. What is your understanding of SQL Server performance tuning and optimization?
SQL Server performance tuning and optimization involves identifying and resolving performance bottlenecks in SQL Server databases to improve query response times and overall system performance. Here are some key steps in this process:
Monitoring performance: Regularly monitoring the performance of SQL Server using tools such as SQL Server Management Studio or third-party performance monitoring tools.
Identifying bottlenecks: Analyzing the performance metrics to identify slow-performing queries, wait statistics, and other performance bottlenecks.
Query optimization: Improving the performance of slow queries by optimizing the query design, adding indexes, and modifying the database schema.
Index optimization: Regularly reviewing and optimizing the indexes to ensure they are being used effectively and improving query performance.
Server configuration: Configuring the SQL Server instance to ensure it is optimized for the specific workloads and hardware.
Stored procedure optimization: Reviewing and optimizing stored procedures to improve the performance of commonly used operations.
Database maintenance: Regularly performing database maintenance tasks, such as updating statistics, rebuilding indexes, and defragmenting disks, to maintain optimal performance.
Monitoring and continuous improvement: Continuously monitoring the performance of the SQL Server instance and making changes to address any new performance bottlenecks that arise.
SQL Server performance tuning and optimization is an ongoing process that requires a deep understanding of SQL Server architecture, query optimization techniques, and database administration best practices. It can significantly improve the performance and scalability of SQL Server databases and support the evolving needs of the business.
9. Can you explain the difference between a clustered and a non-clustered index in SQL Server?
A clustered index in SQL Server determines the physical order of data in a table, while a non-clustered index provides a fast way to look up data in a table without physically rearranging it.
A clustered index uses the column or columns included in the index as the key to sort and store the data rows in the table. There can only be one clustered index per table because the data can only be physically sorted in one way.
A non-clustered index, on the other hand, stores the index data in a separate structure (the index) from the actual data rows, with a pointer to the location of each row in the table. Multiple non-clustered indexes can be created on a single table.
In summary:
- Clustered index: physically sorts the data in the table based on the index key, only one per table.
- The non-clustered index provides a fast lookup mechanism without physically rearranging the data, multiple allowed per table.
10. How do you handle data integrity and data quality in your projects?
Handling data integrity and data quality in projects involves several steps:
- Define data quality requirements: Determine the business rules and data quality criteria that must be met, such as accuracy, completeness, consistency, and timeliness.
- Data validation: Implement data validation rules to ensure that incoming data meets the defined data quality criteria. This can include checks for missing values, data type validation, range checks, and cross-field consistency.
- Data cleaning: Use data cleaning techniques to address data quality issues such as duplicate records, incorrect values, and inconsistent formatting.
- Data enrichment: Enhance the data by adding missing values or transforming it to a more suitable format for analysis.
- Data governance: Establish data governance policies and procedures to ensure data quality is maintained over time. This can include regular data audits, data quality monitoring, and continuous improvement processes.
- Data backup and recovery: Implement backup and recovery processes to ensure that data is protected in the event of hardware failure or other disruptions.
- By implementing these steps, data integrity and data quality can be maintained throughout the project, improving the reliability and usefulness of the data for decision-making and analysis.
11. How a data warehouse varies from an operational database?
This may be recognized as an entry-level question. You’ll answer by declaring that databases utilizing Insert, Delete SQL statements, and Update is regular operational databases that concentrate on pace and efficiency. As a consequence, examining data can be more difficult. On the other hand, With a data warehouse, calculations, aggregations, and picked statements are the prime focus.
12. What are the three types of integration runtimes?
- Azure Integration Run Time: It can copy data among cloud data repositories and it can express the exercise to a type of computing services like SQL server or Azure HDinsight where the transformation takes place
- Self-Hosted Integration Run Time: It is software with basically the equivalent code as Azure Integration Run Time. Except you install it on an on-premise instrument or a virtual machine in the virtual network. A Self Hosted IR can operate copy exercises between a data store in a private network and a public cloud data store.
- Azure SSIS Integration Run Time: With this, one can natively perform SSIS packages in a controlled environment. So when we elevate and shift the SSIS packages to the data factory, we work Azure SSIS Integration Run Time.
13. Differentiate between structured data and unstructured data.
| Parameter | Structured Data | Unstructured Data |
| Storage | DBMS | Unmanaged file structures |
| Standard | ADO.net, ODBC, and SQL | STMP, XML, CSV, and SMS |
| Integration Tool | (Extract, Transform, Load) ELT | Manual data entry or batch processing that incorporates codes |
| scaling | Schema scaling is hard | Scaling is easy. |
14. Describe all portions of a Hadoop application.
- Hadoop Common: It is an arrangement of tools, mechanisms, and libraries that are practiced by Hadoop.
- HDFS: This application recognizes the document structure where the Hadoop data maintain. It is a compelling document framework possessing high data transfer capability.
- Hadoop MapReduce: This is based on the algorithm for the ordering of big-scope data development.
- Hadoop YARN: It is utilized for asset management within the Hadoop group. It can be used for duty scheduling for clients.
15. Can you walk us through your experience with data modeling and database design?
Data modeling is the process of creating a conceptual representation of data, including entities and relationships, to support the needs of a specific use case or application. It involves identifying the data requirements, defining the data elements and their relationships, and creating a visual representation of the data structure.
Database design is the process of applying the data model to create a specific database implementation, including defining the database schema, choosing appropriate data types and structures, and establishing relationships and constraints.
Data modeling and database design are important steps in the development of any database-driven application, as they help ensure that the data is organized in a way that supports the required data access patterns and business rules. A well-designed database can improve the performance, scalability, and maintainability of the application.
16. What is the boundary on the number of integration runtimes?
In a data factory, there is no limit to the number of integration runtime situations. However, the integration runtime can only work with a certain number of VM cores per subscription for SSIS package performance.
17. How would you approach creating a disaster recovery plan for a large-scale data processing environment?
Creating a disaster recovery (DR) plan for a large-scale data processing environment involves the following steps:
Assessing the risks: Identifying the potential sources of data loss, such as natural disasters, hardware failures, or cyberattacks, and determining the impact of such events on the data processing environment.
Defining the requirements: Establishing the recovery time objective (RTO) and recovery point objective (RPO) for the data processing environment, and determining the critical systems and data that need to be recovered in the event of a disaster.
Planning the DR strategy: Deciding on the DR strategy, such as active-active, active-passive, or backup and restore, and defining the specific steps that need to be taken to recover from a disaster.
Building the DR environment: Setting up the DR environment, which may involve duplicating the production environment in a separate location or using cloud-based disaster recovery solutions.
Testing the DR plan: Regularly testing the DR plan to ensure that the DR environment is functional and the recovery process is well understood.
Updating the DR plan: Continuously updating the DR plan to reflect changes in the data processing environment and to ensure that the DR plan is aligned with the evolving business requirements.
Communicating with stakeholders: Regularly communicating the DR plan to stakeholders and ensuring that all relevant personnel are aware of their roles and responsibilities in the event of a disaster.
A well-designed disaster recovery plan can help ensure that critical systems and data can be quickly restored in the event of a disaster, minimizing downtime and minimizing the impact on the business. A comprehensive DR plan requires careful planning, testing, and regular updates to ensure that it remains relevant and effective over time.
18. How do you describe Blocks and Block Scanner in HDFS?
- Blocks are the smallest part of data files. Hadoop simply parts various files into small pieces.
- Block Scanner monitors the record of blocks that are proposed on a DataNode.
19. Can you explain your experience with big data technologies such as Hadoop and Spark?
Hadoop and Spark are two popular big data technologies used for processing and analyzing large amounts of data.
Hadoop is an open-source framework for distributed storage and processing of big data. It consists of the Hadoop Distributed File System (HDFS) for storing data, and the MapReduce programming model for processing data in parallel across a cluster of computers.
Spark is an open-source, in-memory big data processing engine that was designed to improve upon the limitations of Hadoop’s MapReduce. Spark processes data much faster than MapReduce by keeping data in memory, and it provides high-level APIs in Scala, Python, Java, and R, as well as support for SQL, streaming, and machine learning.
Both Hadoop and Spark are widely used for big data processing and analysis and are often used together in a big data ecosystem. Hadoop provides a scalable and reliable platform for storing and processing large amounts of data, while Spark provides a fast and flexible engine for data processing and analysis.
In summary, Hadoop provides a robust and scalable platform for big data processing, while Spark offers a fast and flexible engine for large-scale data processing and analysis.
20. What do you understand by blob storage?
Blob Storage in Azure is a service for saving large numbers of unregulated object data, such as binary data or text. You can practice Blob Storage to disclose data publicly to society or to save application data confidentially.
21. Have you worked with cloud-based data storage and processing solutions such as Azure Data Lake or Azure HDInsight?
Azure Data Lake and Azure HDInsight are cloud-based data storage and processing solutions offered by Microsoft Azure.
Azure Data Lake is a scalable and secure data lake that allows you to store and analyze large amounts of data from various sources, including structured, semi-structured, and unstructured data. It provides a single repository for all your data and enables you to run big data analytics and machine learning algorithms on the data.
Azure HDInsight is a fully-managed cloud service that makes it easy to process big data using popular open-source frameworks such as Apache Hadoop, Apache Spark, and Apache Hive. It provides a platform for storing and processing big data in the cloud and enables you to run big data analytics and machine learning algorithms on the data.
Both Azure Data Lake and Azure HDInsight provide a scalable, secure, and cost-effective solution for big data storage and processing in the cloud. They allow you to store and process large amounts of data without the need for expensive hardware and infrastructure, and provide a platform for running big data analytics and machine learning algorithms on the data.
22. What are the common uses of Blob storage?
Common works of Blob Storage consist of:
- Laboring images or documents straight to a browser
- Saving files for shared access
- Streaming audio and video
- Collecting data for backup and recovery disaster restoration, and archiving
- Saving data for analysis by an on-premises or Azure-hosted
23. Name several XML configuration files in Hadoop.
XML configuration files in Hadoop are:
- Core-site
- Mapred-site
- HDFS-site
- Yarn-site
24. List out the actions for building the ETL process in Azure Data Factory.
- Build a Linked Service for the source data store (SQL Server Database)
- Suppose that we have a cars dataset
- Formulate a Linked Service for address data store which is Azure Data Lake Store
- Build a dataset for Data Saving
- Formulate the pipeline and attach copy activity
- Program the pipeline by combining a trigger
25. What is your experience with ETL processes and tools such as SSIS?
ETL (Extract, Transform, Load) is a process of extracting data from various sources, transforming it into a suitable format for analysis, and loading it into a target database or data warehouse.
SQL Server Integration Services (SSIS) is a tool developed by Microsoft for building and executing ETL processes. It provides a graphical interface for designing and managing data extraction, transformation, and loading operations, as well as data flow and control flow tasks. SSIS supports a wide range of data sources and destinations and provides various transformations for data cleansing, aggregation, and enrichment.
26. Can you specify the necessary applications and frameworks for data engineers?
This question estimate whether the candidate understands the significant requirements for the job and has the aspired technical abilities. In your answer, certainly, specify the titles of frameworks with your level of experience with each.
27. How can I register a pipeline?
- One can utilize the time window trigger or scheduler trigger to program a pipeline.
- The trigger practices a wall-clock calendar program, which can schedule pipelines systematically or in calendar-based recurrent models (for instance, on Mondays at 6:00 PM and Thursdays at 9:00 PM).
28. Can you give an example of a complex data analysis project you worked on and how you approached it?
An example of a complex data analysis project could be a Fraud Detection System for a financial institution. The project would involve the following steps:
- Data collection: Collect and store large amounts of transaction data from various sources such as credit card transactions, ATM transactions, and online banking transactions.
- Data cleaning: Clean and preprocess the data to remove duplicates, missing values, and outliers.
- Data exploration: Explore the data to gain insights into the patterns and trends in the data, and identify any anomalies or suspicious transactions.
- Feature engineering: Extract relevant features from the data to be used for modelings, such as transaction amount, location, and time of day.
- Model building: Build and train machine learning models such as decision trees, random forests, or neural networks to identify fraudulent transactions.
- Model evaluation: Evaluate the performance of the models using metrics such as accuracy, precision, recall, and F1 score.
- Model deployment: Deploy the best-performing model into production, and integrate it with the financial institution’s systems to detect fraudulent transactions in real time.
- Monitoring and maintenance: Monitor the performance of the fraud detection system and make necessary updates and improvements to keep it effective over time.
This project would involve the use of big data technologies, machine learning algorithms, and data visualization tools to analyze large amounts of data and identify fraudulent transactions. The end goal would be to develop a robust and scalable fraud detection system that helps the financial institution detect and prevent fraudulent activities, protecting its customers and their assets.
29. How do you stay current with new data technologies and industry trends?
Staying current with new data technologies and industry trends can be done in several ways, including:
- Regularly reading industry publications and blogs to stay informed on the latest developments.
- Attending conferences, webinars, and workshops related to data technology.
- Participating in online forums and discussion groups to exchange ideas and knowledge with others in the field.
- Taking online courses and certifications to continuously improve skills and knowledge.
- Networking with other professionals in the data technology industry.
- Experiment with new technologies to gain hands-on experience.
- Staying updated with the latest releases and updates from technology vendors and open-source projects.
- Following thought leaders and experts in the field on social media and online platforms.
- By incorporating these activities into your routine, you can stay up-to-date with new data technologies and industry trends, and continue to grow and improve as a professional in the field.
30. Can I transfer the parameters to a pipeline run?
- Yes, parameters are a first-class, top-level theory in Data Factory.
- You can determine parameters at the pipeline level and transfer arguments as you perform the pipeline run on-demand or by using a trigger.
31. Do you have any experience in Java, Python, Bash, or any other scripting languages?
This question highlights the importance of understanding scripting languages as a certified data engineer. It is necessary to have a thorough knowledge of these scripting languages, as it enables you to complete analytical tasks perfectly and automate data flow.
32. Describe the characteristics of Hadoop.
- It is an open-source structure that is ready for freeware.
- Hadoop is cooperative with the various types of hardware and simple to access distinct hardware within a particular node.
- It encourages faster-distributed data processing.
- It saves the data in the group, which is unconventional of the rest of the operations.
- Hadoop supports building replicas for every block with separate nodes.
33. Can I determine default rates for the pipeline parameters?
You can determine default values for the parameters in the pipelines.
34. What are the necessary skills to become a data engineer?
- Complete knowledge of Data Modelling.
- Understanding database architecture and database design.
- Working practice in data stores and distributed systems
- Data Visualization abilities.
- Proficiency in Data Warehousing and ETL Tools.
- Outstanding critical thinking, leadership, communication, and problem-solving abilities.
35. Explain the principal methods of Reducer.
- setup (): It is utilized for configuring parameters like the intensity of input data and scattered cache.
- cleanup(): This design is utilized to clean temporary files.
- reduce(): It is a center of the reducer which is summoned once per key with the linked decreased task.
36. Can you describe your experience with data security, privacy, and governance?
Data security refers to the measures taken to protect sensitive and confidential information from unauthorized access, use, disclosure, disruption, modification, or destruction. Key aspects of data security include access controls, data encryption, firewalls, and incident response plans.
Data privacy refers to the protection of personal information, such as name, address, Social Security number, etc. Key aspects of data privacy include data minimization, data retention policies, and secure data transfer methods. Data privacy regulations, such as the European Union’s General Data Protection Regulation (GDPR), set standards for the collection, storage, and use of personal information.
Data governance refers to the policies, procedures, and processes that organizations put in place to manage their data assets. Key aspects of data governance include data quality, data lineage, metadata management, and data architecture. Effective data governance helps organizations ensure that their data is accurate, consistent, and reliable, and that it is used in compliance with legal and regulatory requirements.
37. Does an activity output property be employed in different activities?
An activity output can employ in a subsequent activity with the @activity construct.
38. Distinguish between a Data Engineer and Data Scientist.
By this question, the interviewer is seeking to assess your knowledge of different job positions inside a data warehouse team. The abilities, skills, and duties of both professions often overlay, but they are different from each other.
39. How have you worked with data scientists and data analysts to understand their requirements and implement solutions?
When working with data scientists and data analysts, it is important to understand their requirements and goals, as well as their domain-specific knowledge and expertise. To do this, it can be helpful to engage in open and frequent communication, ask clarifying questions, and actively listen to their needs.
In terms of implementing solutions, it is crucial to collaborate closely with data scientists and data analysts to ensure that their requirements are being met and that the solutions being developed align with their goals and objectives. This may involve working with them to determine the best technical approach, incorporating feedback into solution design, and ensuring that the solution is easily maintainable and scalable.
In addition, it may be necessary to work with data scientists and data analysts to ensure that the data being used is accurate, consistent, and of high quality, and that the data processing and storage systems are secure and reliable. This may involve conducting data validation and testing, and ensuring that the appropriate data privacy and security measures are in place.
Ultimately, the key to successfully working with data scientists and data analysts is to establish a strong partnership built on mutual trust, open communication, and a shared commitment to delivering high-quality solutions that meet their requirements.
40. How do I delicately manage null values in an activity output?
We can work the @coalesce construct handle the null values delicately.
41. Can you share examples of how you have automated data processing workflows?
Automating data processing workflows can help to streamline data processing and increase efficiency, as well as reduce the risk of errors and increase accuracy. Some examples of how data processing workflows can be automated include:
Using scripting languages such as Python or R to automate data processing tasks, such as data extraction, transformation, and loading (ETL).
Implementing a data pipeline using a cloud-based data processing platform, such as Apache NiFi or Apache Beam, to automate the flow of data from various sources to the target systems.
Using tools such as Apache Airflow or AWS Glue to schedule and orchestrate data processing workflows, including scheduling periodic tasks and triggering workflows based on specific conditions.
Using machine learning algorithms to automate data processing tasks, such as data classification, clustering, and anomaly detection.
Implementing an event-driven architecture, such as Apache Kafka, to automate the processing of real-time data streams and trigger workflows based on specific events.
In each of these examples, the goal is to automate the processing of large volumes of data in a repeatable, scalable, and efficient manner, while ensuring the accuracy and consistency of the data being processed.
42. According to you, what are the everyday duties of a data engineer?
- Construction, testing, and preservation of architectures.
- Arranging the plan with business requisites.
- Data acquisition and expansion of data set methods.
- Expanding statistical models and machine learning.
- Recognizing ways to enhance flexibility, data reliability, accuracy, and quality.
43. Which Data Factory version do you practice to perform data flows?
Use the Data Factory V2 version to create data flows.
44. What will be your strategy for generating a new analytical output as a data engineer?
From this question, the hiring administrators want to know your position as a data engineer in producing a new product and estimate your knowledge and understanding of the product advancement cycle. As a data engineer, you constrain the result of the final product as you are answerable for organizing algorithms or metrics with the exact data.
Your first move would be to learn the outline of the whole product to understand the whole wants and scope. Your next step would be seeing into the details and causes for every metric. Consider about as many issues that could happen, and it assists you to generate a more strong system with a proper level of granularity.
45. How you would extend a big data solution?
- 1) Integrate data practicing data resources like SAP, MySQL, RDBMS, Salesforce.
- 2) Store data obtained data in either the HDFS or the NoSQL database.
- 3) Expand big data solutions using processing structures like Spark, Pig, and MapReduce.
46. What has transformed from private preview to inadequate public preview in respect to data flows?
- You will not have to bring your own Azure Databricks clusters.
- Data Factory will manage cluster creation and tear-down.
- You can however use Data Lake Storage Gen2 and Blob storage to save those files. Use the proper linked assistance for those storage engines.
47. How have you automated data processing workflows?
To automate data processing workflows, a data engineer may use the following steps:
Identify the data processing tasks that can be automated: The first step is to identify the data processing tasks that can be automated and that will bring the most value to the business.
Choose the right tools: Based on the data processing tasks, the data engineer must choose the right tools for the job. There are various tools available, including data integration tools like Apache NiFi, Apache Airflow, or Apache Beam, and big data tools like Apache Hadoop and Apache Spark.
Design and implement the data processing pipeline: The next step is to design and implement the data processing pipeline. This involves defining the data sources, data transformations, and data destinations. The pipeline should be designed to be scalable and resilient, with appropriate error handling and logging in place.
Schedule and run the workflows: Once the data processing pipeline is designed and implemented, it should be scheduled and run to perform the desired data processing tasks. This can be done manually or automatically, depending on the requirements.
Monitor and optimize the workflows: The final step is to monitor the workflows and optimize them if necessary. This involves analyzing the performance of the workflows, identifying bottlenecks, and making improvements where necessary.
By automating data processing workflows, a data engineer can help organizations to become more efficient, reduce errors, and improve data quality.
48. What mechanisms did you practice in a current project?
Interviewers want to evaluate your decision-making abilities and knowledge about various tools. Therefore, use this problem to justify your rationale for determining special tools over others.
- Walk the employing managers by your thought method, describing your reasons for acknowledging the particular tool, its advantages, and the disadvantages of other technologies.
49. Discriminate among Star and Snowflake Schema.
| Star | SnowFlake Schema |
| Dimensions hierarchies are saved in the dimensional table. | Every hierarchy is stored in separate tables. |
| The possibilities of data redundancy are great | The possibilities of data redundancy are moderate. |
| It has a very easy DB design | It has a complicated DB design |
| Give a quicker process for cube processing | Cube processing is delayed due to the complicated join. |
50. Describe Hadoop distributed file system.
Hadoop operates with scalable classified file systems like HFTP FS, S3, FS, and HDFS. Also, Hadoop Distributed File System is built on the Google File System. This filing method is created in a means that it can simply run on a wide cluster of computer systems.
51. How to deliver security in Hadoop?
- 1) The initial step is to ensure the authentication channel of the customer to the server. Accommodate time-stamped to the client.
- 2) In the following step, the client practices the received time-stamped to demand TGS for a service ticket.
- 3) In the end step, the client uses a service ticket for self-authentication to a special server.
52. What are the problems you faced during your previous projects?
Answer this using the STAR method
- Situation: Notify them about the conditions due to which the problem occurred.
- Task: It is necessary to develop your role in defeating the problem. For instance, if you held a leadership position and produced a working solution, then showing it could be emphatic if you were talking for a leadership job.
- Action: Exercise the interviewer by the steps you used to settle the problem.
- Result: Always describe the outcomes of your actions. Talk about the pieces of knowledge and insights obtained by you.
53. Have you ever before transmuted unstructured into structured data?
It is an essential question as your response can express your knowledge of both the data models and your practical working background. You can answer this by casually differentiating between both sections. The unstructured data must be converted into structured for precise data analysis, and you can consider the techniques for transformation. You must bestow real-world circumstances wherein you traded the unstructured into structured data. If you are a graduate and don’t have a professional background, present information associated with your academic plans.
54. How would you approve a data movement from one database to different?
The efficacy of data and guaranteeing that no data is released should be of the highest priority for a data engineer. Hiring administrators examine this question to know your thought method on how validation of data would occur.
The candidate should be capable to talk about proper validation representations in different situations. For example, you could recommend that validation could be a simplistic comparison, or it can occur after the comprehensive data migration.
55. Why are you planning for the Data Engineer position in our company especially?
By this inquiry, the interviewer is striving to see how well you can convince them concerning your experience in the subject, besides the necessity for practicing all the data methodologies. It is always an authority to already know the job designation in particular, along with the company’s return and aspects, thereby achieving a comprehensive knowledge of what tools, every subject methodologies are needed to work in the role triumphantly.
One of the most reliable ways to crack your job interview is to get regular training and receive your certification. If you’re an ambitious data engineer, register in our Data Engineering Course now and get originated by acquiring the skills that can assist you to land your ideal job. We hope, this article helped! Stay safe and practice with Testpreptraining!
