Top 90+ Data Engineer Interview Questions and Answers

Data engineering is one of the most attractive sectors that play a great role in maintaining and processing large amounts of data for corporations. Thus, as data-driven decision-making becomes more and more significant for organizations, the demand for suitably skilled data engineers continues to mount. Therefore, if you are preparing for a data engineer interview, it is essential that one understands various concepts and technologies related to data processing, storage, and analysis. 

The article will cover over 90+ Data Engineering interview questions, from simpler concepts to advanced topics. Therefore, whether you are a fresher or have experience, these questions will help you with preparing for the next data engineering interview. In case you are aspiring for data engineering and wish to learn more about the deeper aspects such as work, career, salaries etc. I encourage you to have a look through our blog entitled Data Engineer Salary. 

So, if the technicalities are beyond your understanding, please join our Google Cloud courses.   

1. What is Data Engineering?

The practice of data engineering deals with the designing, building, and maintenance of systems for collecting, storing, and processing large volumes of data. Data engineering deals with ensuring that data is clean, reliable, and available for analysis. Typically, the engineers will use databases, ETL pipelines, and cloud platforms to optimize the flow of data. 

2. Define Data Modeling.

Data modeling refers to making a conceptual representation of data structures and their relationships within a system. This model would then say how the data gets logically organized, how it is stored, and how it is accessed. Models are also important to ensure the data is applied and maintained in a consistent, accurate, and efficient manner. 

3. What are some of the design schemas used when performing Data Modeling?

There are 3 Schemas used when performing data modeling and they are as follows: 

Relational Database Schemas 

• Star Schema- And very simple & fast for query performance with only a central fact table relative to details. 

• Snowflake Schema- The dimension tables are normalized, significantly reducing redundancy but making it quite complex. 

• Galaxy Schema- Multiple fact tables sharing dimensions to facilitate complex analytics. 

• 3NF Schema- Highly normalized to maintain data integrity, is used in OLTP systems.  

NoSQL Database Schemas 

• Key-Value Schema- Where simple key-value pairs allow quick-lookups (Ex: Redis). 

• Document Schema- Stores JSON/BSON docs, with dynamic structures (Ex: MongoDB). 

• Column-Family Schema- Organized based on the read/write patterns (Ex: Cassandra). 

• Graph Schema- Using nodes and edges to define relationships (Ex: Neo4j).  

Data Warehouse & Big Data Model 

• Lambda Architecture- Merges batch and real-time processing. 

• Delta Lake Schema- Optimized to suit both structured and unstructured data. 

4. What are the differences between structured and unstructured data?

Parameter	Structured Data	Unstructured Data
Definition	Organized, formatted data stored in predefined schemas	Raw, unorganized data without a fixed structure
Format	Tabular (rows & columns)	Free-form (text, images, videos, etc.)
Storage	Relational Databases (SQL)	NoSQL, Data Lakes, Object Storage
Data Model	Schema-based (e.g., Star Schema, 3NF)	No predefined schema
Examples	Customer databases, financial transactions	Emails, social media posts, multimedia files
Processing	Easily queried with SQL	Requires advanced processing (AI, NLP, ML)
Scalability	Scales vertically (more power to a single server)	Scales horizontally (distributed storage)
Flexibility	Rigid, predefined structure	Highly flexible and adaptable
Use Cases	Banking, ERP, Inventory Management	Social Media, IoT, Big Data Analytics

5. What is Hadoop? Give Brief Description about This.

Hadoop is an open-source framework for distributed storage and processing of vast datasets in clusters of commodity hardware. HDFS serves the purpose of large-scale data storage, while MapReduce permits parallel processing with strong consolation for fault tolerance and very high scalability. The major area where Hadoop is applied is big data analytics and machine learning applications. Because Hadoop can accommodate heterogeneous data types and process them well, it comes into play in many other areas like large-scale data processing.  

6. What are some important components of Hadoop?

The following components are within the Hadoop ecosystem: 

• Hadoop Common: These are a set of libraries and utilities that support other Hadoop modules. 

• HDFS(High-Performance Hadoop Distributed File system): In a distributed way, it makes possible high bandwidth storage of data on many nodes.  

• YARN: This is an acronym for "Yet Another Resource Negotiator." It is responsible for scheduling jobs and working with the resource management in Hadoop cluster, thus allowing efficient execution of jobs. 

• MapReduce: A programming model for processing a large volume of data by dividing work into map and reduce steps. 

7. What is a NameNode in HDFS?

The NameNode functions as the master node of an HDFS. It takes the responsibility for maintaining the metadata, e.g., directory structures of files and block locations within DataNodes. The NameNode tracks the policies ensuring the integrity and availability of data, though it does not store the data itself. So it knows where the data is, and how it is being distributed and replicated across DataNodes. 

8. What is Hadoop Streaming?

Hadoop Streaming is a utility that allows users to create MapReduce in any executable or script so that it will process in a Hadoop cluster without requiring any knowledge of Java programming. Thus, it lets different tools for data processing, which may or may not be written in Java, to easily be integrated into the Hadoop ecosystem.  

9. What are Some Significant Features of Hadoop?

Hadoop is notorious for several of its chief features: 

• Open-source framework: All development is governed under the auspices of the Apache Software Foundation. 

• Distributed computing: Processes the data using clusters of commodity hardware. 

• Parallel processing: Speeding up data processing by performing multiple executions concurrently. 

• Redundant data storage: Ensuring availability of data by replicating data across nodes. 

10. What are the Four Vs of Big Data?

The four Vs of Big Data: 

• Volume: Represents huge amounts of data-generated. 

• Variety: Heterogeneous data types and formats. 

• Velocity: The pace of data generation and processing. 

• Veracity: Concerned with data validity and its trustworthiness.  

11. What is a Block and Block Scanner in HDFS?

Blocks are the minimum data storage unit in HDFS, normally either 128 M or 256 MB sized. Block scanners are used to check the integrity of all blocks stored in DataNodes, ensuring data consistency by detecting corrupted data and helping with recovery.  

12. How Does Block Scanner Work in Dealing with Corrupt File?

Whenever a block scanner discovers corrupted data, it will report to the NameNode. The NameNode would then execute its procedures for block replication using the appropriate replication policy. 

13. How does the NameNode have conversation with DataNode?

DataNameNode communicates with the DataNodes with the help of the following messages that are sent intermittently:- 

• Block reports: Update about the stored blocks is sent by them. 

• Heartbeats: Signals of periodic nature sent to show digits as available DataNode. 

14. What is COSHH? 

COSHH is also known as Classifications and Optimal Scheduling for Spatiotemporally Mixed Heterogeneous Hadoop environments. It is a class scheduling framework, which chiefly works in optimizing the final job completion times in heterogeneous Hadoop systems by task-classification and system resources-based scheduling. 

15. What is Star Schema, in brief?

A star schema is known as the data warehouse schema which has a single central fact table surrounded with dimension tables. It is used to make complex queries simple and straightforward wherein the data is organized in a star-like pattern, which enhances the performance in executing queries for data analysis. 

16. Give a Brief description of Snowflake.

The snowflake schema is an expansion of the star schema, with additional dimensions further normalized into sources with multiple levels of related tables. The snowflake schema takes this form to avoid redundancy in data and improves the level of integrity of data and query performance. 

17. State the differences between Star Schema and Snowflake Schema.

Feature	Star Schema	Snowflake Schema
Architecture	Central fact table with denormalized dimension tables.	Central fact table with normalized dimension tables.
Complexity	Simpler to understand and design.	More complex due to normalization and additional sub-dimensions.
Normalization	Denormalized, reducing joins needed.	Normalized, reducing data redundancy but increasing joins.
Performance	Optimized for fast query performance, ideal for simple data structures.	Suitable for complex data relationships but may require more processing time.
Query Maintenance	Easier maintenance as changes mainly affect the fact table.	More challenging maintenance as changes impact multiple tables.
Storage Requirements	Requires more storage due to data redundancy.	Requires less storage due to normalized structure.
Use Cases	Ideal for straightforward data relationships and fast query performance.	Suitable for complex data structures where data redundancy needs to be minimized.

18. What is Big Data?

Big Data relates to volumes and varieties of data that tend to become exceptionally complex as to exceed the limit set by traditional device processing and/or exceed the threshold established to generate them. The Big Data includes structured, semi-structured, and unstructured data that require other technical infrastructure to capture, store, and analyze them. The birth of Big Data was associated with five Vs: volume, variety, velocity, veracity, and value. Those Vs would render the application of valuable insights and patterns drawn from the aggregated data, thereby informing strategic decision-making and improving operational efficiency. 

19. Why do Data Engineers need SQL?

Data Engineers require SQL because they need to use it in tandem with databases in order to have the ability to extract, manipulate and analyze data from those databases. The paper explains the importance of SQL in understanding data relationships and in conducting complex queries that are needed for data modeling, data warehousing, and data integration. 

20. What's a Data Lake?

Data Lake is the central repository to store raw data in its native format. The flexibility of data processing and analysis comes because of the ability to store most types of data structures and types. A Data Lake absorbs large volumes of data and executes many processing tasks simultaneously, making this storage system suitable for big data analytics and machine learning practice. It is also a scalable and cost-effective solution for data storage as well as processing. 

21. How does Cloud Computing help with Data Engineering?

Indeed, the efficiency and cost-effectiveness of every data engineering task receive a very significant boost from cloud computing's demand-scaling provision. It permits data engineers to work on a large data set with many flexible storage options and high-performance computational capabilities as well as built-in tools for automated data processing. The cloud infrastructures entail real-time processing and integration of data, followed by analysis. 

22. What does Data Profiling mean?

Data profiling is the procedure of systematically analyzing data sets with a view to having more comprehensive knowledge regarding the very structure of the data, the quality it possesses, and the complete content available for return. This will point to missing data, inconsistent data, incorrect data, and correct patterns or use, which are necessary for data quality improvement, transformation processes, and integration. Data profiling is critical in determining the reliability of data before further analysis or used processing steps. 

23. Why use Data Warehouses?

A Data Warehouse acts as a centralized repository, integrating data from many sources into a single structure for analysis and reporting. Since Data Warehousing allows decision-makers to perform complex queries and data mining operations on vast quantities of historical data, their main use is to assist organizations in decision-making. By providing a structured environment for data analysis, Data Warehouses facilitate business intelligence activities thus increasing the efficiency of operations and insights into the business. 

24. What is Data Redundancy, and how is it fixed?

Data Redundancy is the situation in which duplicate data is stored in more than one location, and such duplication would lead to inconsistencies and inefficiencies. To tackle Data Redundancy, data normalization, and data governance practices are used to organize data in such a way as to eliminate duplication and ensure consistency. Other approaches like data deduplication and data integration further preserve data integrity by eliminating redundant data, thus giving advantages in terms of storage optimization and enhancing data quality. 

25. Name the XML configuration files present in Hadoop.

The primary XML configuration files available in Hadoop include the following: 

• core-site.xml: This file is used for configuration settings specifically for the Hadoop core (the NameNode location, a temporary data storage location, etc.). It provides key parameters indicating their values, including fs.defaultFS, which specifies the default filesystem URI. 

• mapred-site.xml: Configures MapReduce settings, including the framework name (for example, YARN) and other job execution parameters. 

• hdfs-site.xml, which contains the different configuration properties for HDFS, for example, replication factors of blocks and DataNode paths. 

• yarn-site.xml: These configurations are primarily associated with YARN, such as resource allocation, and configurations on NodeManagers. 

26. What is the meaning of FSCK?

FSCK refers to File System Check, and it is a very important command in the HDFS because it is used for checking and reporting different inconsistencies/errors from the entire filesystem. It can report on corrupted blocks or missing replicas or wrong block counts, and it has detailed information that can be used to repair the filesystem. 

27. What is ETL and why is it important in data engineering?

ETL or Extract, Transform, and Load, is the basic process of data engineering, which performs extraction from different data sources and transforms the data into a single format and transfers it to a destination, usually a data warehouse or data lake. ETL is relevant because this data entered into the organizations aids in collection, cleaning, and structuring data from different sources to be useful for both analysis and decision-making. Without ETL, data remains in its raw form and therefore cannot be effectively useful. 

28. Explain the difference between a data warehouse and a data lake.

A data warehouse is a structured repository typically designed for database querying and reporting in which stored data is generally structured and follows a predefined schema. This schema is optimized for analytical use and decision support. In comparison, a data lake is not schema-required structure but flexible storage that enables the ingestion of stored data in a semi-or nonsystematic mode. Phenomenally ideal for big data storage and exploration, it uses schema on read approaches. 

29. What is a primary key, foreign key, and how are they used in database design?

A primary key is a unique value that identifies every row in a particular database table, thereby securing data integrity and providing access to individual records. A foreign key links tables together by pointing back to the primary key of the other table, ensuring the same basic unit of data considered to parent and ensuring referential integrity. These keys form the backbone of a database design to define data organization, establish clearly defined data relationships, and avoid redundancy in data. 

30. What is the CAP theorem, and how does it relate to distributed systems in data engineering?

As per the CAP theorem, every such distributed system can only have two out of Consistency, Availability, and Partition tolerance at a particular point in time. This theorem restrains the design decisions with regard to trade-offs between these attributes. For example, in some event of a network partition, the system can either have the availability or consistency. This is one of the fundamental foundations to actually understand while designing distributed systems, to balance those opposing demands. 

31. What is the purpose of partitioning in distributed data processing frameworks like Hadoop or Spark?

Partitioning in distributed systems like Hadoop or Spark means splitting a dataset into smaller, independent subsets (partitions) so that they can be processed in parallel across the nodes of the cluster. Minimizing the movement of data between different nodes inside the distributed cluster while maximizing data locality and optimizing the distribution of computation will thus enhance its performance. In addition to horizontal partitioning (splits by rows) and hash partitioning (distributes data by means of hash functions), various strategies ensure optimum utilization of resources and scalability during runtime. For instance, Hadoop in HDFS splits a very large file into fixed chunks of a specified size, e.g., 128 MB, whereas Spark splits the data dynamically on partitions to achieve maximum parallelism whenever transformations such as map or reduce operations are invoked. 

32. Explain the concept of data serialization and why it is important in data engineering.

Data serialization means converting complex data structures (objects, records) into a standardized format (say bytes, JSON, Avro) that can be stored, sent, or reconstructed at some other time. This is crucial for: 

• Interoperability: It allows multiple systems to communicate and exchange. 

• Storage friendliness: It compresses data with lossless compression while preserving the schema metadata, such as Parquet and Avro. 

• Performance: In binary serialization, there is less overhead exchange across the network in a distributed system such as Protocol Buffers. 

• Schema evolution: In Avro, backward compatibility and forward compatibility methods help update the pipelines without a hitch. 

33. How do you ensure data quality in a data pipeline, and what are some common data quality issues to watch out for?

In data pipelines, the assurance of data quality covers: 

• Validation: This step enforces constraints (such as null fields, range validity) with the help of tools such as Great Expectations.  

• Cleansing: Duplicate values are erased, formatting inconsistencies are addressed, and missing values are imputed mainly during this stage.  

• Monitoring: Anomaly detection (for example, services like Deequ can heed sudden drops in data volumes and protect them against manipulation).  

Common data quality issues: 

• Missing values can be imputed or excluded based on the context. 

• Schema drift is when the data types do not match or unexpected fields come in, interfering with downstream processes. 

• Data skew means uneven distribution leads to processing bottlenecks. 

34. What is data skew in the context of distributed data processing, and how can it be mitigated?

Data skew means the unequal partition size, which leads to some straggler nodes and performance degradation. Mitigation includes:  

• Custom partitioning: Redefining the data with either the hash function or range-based splits.  

• Salting: Introduces synthetic keys to balance skewed keys, e.g., appending random suffixes.  

• Dynamic repartitioning: Changing the number of partitions during runtime by tools.  

• Optimizing skew joins: Isolating the skew keys from each other for processing, for example, join broadcasting for small ones. 

35. Describe the differences between batch processing and stream processing, and provide use cases for each.

Criteria	Batch Processing	Stream Processing
Data Handling	Processes finite, static datasets in bulk.	Processes unbounded data in real-time.
Latency	High (minutes to hours).	Low (milliseconds to seconds).
Use Cases	Historical analytics  ETL jobs	Real-time fraud detection  IoT monitoring
Tools	Hadoop MapReduce, Spark SQL	Apache Flink, Kafka Streams

36. Can you explain the concept of data lineage and why it is crucial in data engineering and compliance?

Data lineage can be understood as the complete developmental lifecycle of data-from creation till last consumption through the series of transformations and pipelines. It is most important for: 

1. Compliance: auditing data provenance.
2. Debugging: root cause investigation of data anomaly or pipeline failure.
3. Governance: transparency of data use and transformation logic.

Tools provide automatic tracking of lineage over mapped dependency across systems. 

37. What do APIs do in Data Engineering?

APIs standardize interactions between systems. This means that: 

• Data integration: Fetching/ingesting data from third-party platforms. 

• Automation: Triggering pipeline workflows. 

• Microservices: Decoupling components in distributed architectures. 

API-driven access that is security and scalable is guaranteed, while tools deal with a pipeline in an API-related manner. 

38. What is Data Transformation?

Data Transformation describes processes in which data is converted from one format or structure to another, aiming at achieving compatibility for analysis or reporting in wide-ranging systems. Operations such as data cleaning, data aggregation, and data normalization form part of this process in order to bring conformity among data from different sources. 

39. Why is encrypting data important?

Encrypting data ensures protection against unauthorized access. Data is rendered unintelligible (ciphertext), requiring a decryption key to reveal its meaning. Therefore, confidentiality and integrity of sensitive information are ensured, thus protecting data from data breaches and cyber exposure. 

40. How does caching data improve things?

Caching places frequently accessed data in locations where the data can be accessed quickly, thus minimizing latency and promoting quick retrieval of data. Therefore, caching improves the performance of the system when data is actually required while enhancing user experience. 

41. Why index data in databases?

Indexing creates shortcuts for accessing the data from within the database so that queries related to that data can be completed in a short time without full table scans on a larger table. Thus, the efficiency of data retrieval is greatly improved by indexing and is mandatory for databases with high performance. 

42. What does replicating data do?

It aims to maintain data copies in various locations for the sake of consistency and availability because replication functions by keeping the same data set in more than one location. This purpose protects information against loss by having duplicate data sets that can be referred to in the case any loss of the other data. 

43. When is Batch Processing used?

Batch Processing is used when data needs to be processed in huge volumes, in batches, for processes that can tolerate the consequent latency, such as nightly reporting or analysis of historical data. It handles transactions collected over time and processes these transactions together. 

44. What are some of the methods of Reducer?

The methods of Reducer include the following: 

• setup(): Used to set input parameters and get cached. 

• reduce(): This processes key-value pairs in order to produce output. 

• cleanup(): This cleans up temporary files after the processing is over. 

45. What are the different usage modes of Hadoop?

Hadoop runs in three modes: 

• Standalone: Single-node running, as in a non-distributed mode. 

• Pseudo-distributed: Distributed simulation on a single machine. 

• Fully distributed: A multi-node production-oriented environment. 

46. How is data security ensured in Hadoop?

The following are the measures ensuring data security in Hadoop: 

• Authentication: Secured channels between clients and servers. 

• Authorization: Service tickets for access control. 

• Encryption: Protection of data at rest and in transit. 

47. Which are the default port numbers for Port Tracker, Task Tracker, and NameNode in Hadoop?

• Port Tracker: 50030 

• Task Tracker: 50060 

• NameNode: 50070. 

48. How does Big Data Analytics help increase the revenue of a company?

Big Data Analytics increases revenue by: 

• Increasing operations efficiency. Reducing costs. 

• Customer value enhancements. Personalizations and retention strategies. 

• Strategically focused decision-making. Data insights that lead to growth. 

49. In your opinion, what does a Data Engineer majorly do?

A Data Engineer is one who: 

• Handles: Ingestion, processing, and storage management of  

• Data Pipeline. 

• ETL operation: Transformation of data for analysis purposes. 

• Data quality assurance: Cleaning and Redundancy removal. 

50. What are some of the technologies and skills that a Data Engineer should possess?

Key technologies include: 

• Programming languages: Python, SQL, HiveQL. 

• Machine Learning: For the purpose of predictive analytics. 

• Data Structures: Familiarization with data formats like JSON, Avro. 

51. What is the difference between a Data Architect and a Data Engineer?

A Data Architect designs the data; however, a Data Engineer implements it and maintains the data system including its pipeline and infrastructure. 

52. How is the distance between nodes defined when using Hadoop?

Usually the distance between nodes in Hadoop is calculated based on the network topology and mostly the distance will be calculated using getDistance(). 

53. What is the data stored in the NameNode?

The NameNode keeps all the metadata for the HDFS about namespace information and block locations. 

54. What is meant by Rack Awareness?

Rack awareness deals with optimizing data accesses in Hadoop by placing data to nodes that are closer to the requesting client, thus reducing network traffic. 

55. What is a Heartbeat message?

Heartbeat is a message sent by a DataNode to a NameNode from time to time, telling that it is very much alive. 

56. What is the use of a Context Object in Hadoop?

The Context Object enables communication between the mappers and other components of the system, providing access to job configurations and system details. 

57. What is the use of Hive in the Hadoop ecosystem?

Hive is simply the one that provides that easy SQL-like interface in manipulating and querying the data fed in Hadoop and converting the queries to MapReduce jobs. 

58. What is the use of Metastore in Hive?

The Metastore contains schema information and metadata regarding tables in Hive for the management of definitions and mappings for data residing in HDFS, or those stored in other base datasources. 

59. Explain the concept of Data Sharding and how it affects database scalability.

This process allows for horizontal scaling by distributing the data and workloads, thereby reducing query latencies and resource contention. Sharding boost scalability by allowing nodes to be added incrementally, while ensuring steady performance, to cope with the growth of data volume and user load. 

60. How would you design a system to deduplicate streaming data in real-time?

Use probabilistic data structures (Bloom/Cuckoo Filters) to track unique records in memory. Implement time-based windows (e.g., sliding/tumbling) to expire old entries and exactly-once processing guarantees via transactional IDs or watermarking. 

61. Describe the use of Directed Acyclic Graphs (DAGs) in data processing frameworks like Apache Spark.

DAGs represent data transformations by nodes and dependencies by edges. To optimize execution, Spark groups operations into stages for fault tolerance (using lineage) and pipelined execution (to save on disk I/O). 

62. How can eventual consistency be handled in a distributed database system?

Use version vectors to discover conflicts, quorum-based writes (for example W + R > N), and CRDTs for merging or concurrently updating. Conflict resolution can be done via LWW and an application-level reconciliation logic. 

63. Explain how a Bloom Filter works and where it might be used in a data engineering pipeline.

A Bloom Filter is based on the principle of hashing and employs hash functions to map elements onto a bit vector or bit array. It returns either "possibly in set" (there's a risk of false positives) or "definitely not in set." It is being used for pre-filtering in the case of deduplication so that unnecessary disk I/O for checking non-existent keys can be avoided. 

64. How would you implement data retention policies in a data warehouse?

The data warehouse can make use of time-based partitioning using day/month partitions and Time-To-Live (TTL) to make partitions eligible for auto-expiration. Archiving the cold data to cheaper storage such as S3 Glacier will be done and controlling it through a metadata tagging. 

65. Discuss the CAP theorem and its implications for distributed systems.

The CAP states a distributed system will give a guarantee for either Consistency, Availability, or Partition tolerance. Depending on the choice of which two properties to favor:  

• CP (for example, HBase): guarantees consistency when there are partitions but low availability.  

• AP (for example, Cassandra): guarantees availability during partition but gives eventual consistency. 

66. How can skewness be handled during a join operation in a distributed data processing environment?

• Salting: adds random prefixes to heavily appearing keys and redistributes data. 

• Broadcasting Join: Replicate smaller tables to all nodes. 

• Dynamic Partition Pruning: Filter partitions early to reduce shuffling. 

67. Explain how a Time-series Database is different from a traditional Relational Database and provide examples.

Time-series DBs (e.g., InfluxDB and TimescaleDB) optimize for high-volume writes, time-range queries, and retention policies, while Relational DBs (e.g., PostgreSQL) optimize for ACID transactions and complex joins. 

68. How would you ensure data quality and integrity while ingesting data from multiple heterogeneous sources?

• Via schema validation (such as Avro schema). 

• Data profiling for revealing anomalies. 

• Idempotent writes and checksums for integrity. 

• Logger metrics (null rates, for instance) for monitoring purposes. 

69. What are the components that are available in the Hive data model?

Following are some of the components in Hive: 

• Tables: Logical containers of data. 

• Partitions: Subdividing by columns (for example; date). 

• Buckets: Hashing techniques used for random sampling. 

70. Can you create more than a single table for an individual data file?

Yes. The Hive metastore decouples the schema from the storage; therefore, more than one table, with different schemas, can reference the same underlying HDFS file. 

71. What is the meaning of Skewed tables in Hive?

Tables with uneven distribution of column values. Hive optimizes storage by separating frequent values into their directories. 

72. What are the collections that are present in Hive?

Hive has the following collections/data types: 

• Array: Ordered element collection.  

• Map: Key-value pairs.  

• Struct: Nested fields.  

• Union: Heterogeneous data types. 

73. What is SerDe in Hive?

SerDe (Serialization/Deserialization) converts HDFS formats (CSV, JSON, etc.) into Hive's in-memory Java objects and vice versa. Custom SerDes take care of complex formats (such as Avro). 

74. What are the table creation functions present in Hive?

Following are some of the table creation functions in Hive: 

• Explode(array/map): Unnest nested structures.  

• JSON_tuple(): Extract fields from JSON.  

• Stack(): Pivot rows into columns. 

75. What is the role of the .hiverc file in Hive?

It initializes Hive CLI sessions by setting configuration parameters (e.g.,hive.cli.print.header=true) and pre-loading UDFs. 

76. What are *args and **kwargs used for?

1. *args - For variable positional arguments (e.g., func(a, *args)). 

1. **kwargs - For capturing keyword arguments in the form of a dictionary (for example, func(a, **kwargs)). 

77. How can you see the structure of a database using MySQL?

Use the DESCRIBE table_name; command to display column names, types, and constraints. 

78. Can you search for a specific string in a column present in a MySQL table?

Yes, Use REGEXP in sql - SELECT * FROM table WHERE column REGEXP 'pattern';  

79. In brief, what is the difference between a Data Warehouse and a Database?

The Data Warehouse is usually a repository used for analytical querying, aggregation, and reporting of historical data that has been structured to accommodate very complex queries and business intelligence. On the contrary, Database keeps a finger on the transactional processes with respect to the ACID principles of maintaining and manipulating data. 

80. Have you earned any sort of certification to boost your opportunities as a Data Engineer?

Certifications show that a person is committed to learning and mastering a skill. They give a platform to project expertise around certain technologies and methodologies-given that these can be either Hadoop, Spark, or cloud platforms that may impact a career. 

81. Do you have any experience working in the same industry as ours before?

Working in the same sector gives recognition and context for understanding certain challenges and technologies. Mention relevant tools and techniques for the prior jobs that can prove your punctuation for performing similar tasks. 

82. Why are you applying for the Data Engineer role in our company?

Demonstrate skills in areas such as data engineering in Google Cloud, data pipelines, ETL processes, and data architecture. Try to associate the roles to the company's technology stack so that it seems to fit within the organization's needs. 

83. What is your plan after joining this Data Engineer role?

Plan to study the data infrastructure of the company, look for avenues of improvement, and suggest enhancements. Improving data pipelines, data quality, and introducing new technology are areas of focus. 

84. Do you have prior experience working with Data Modeling?

Basically, Data modeling is the process of planning data structures that will serve the specific needs of a business. Experience with tools like Pentaho or tools like Informatica would be advantageous. Talk about your knowledge of data normalization, ERDs, and concepts of data warehousing. 

85. Discuss the implications of the General Data Protection Regulation (GDPR) on data engineering pipelines and how to ensure compliance.

GDPR would require anonymity, encryption of data processing, and obtaining relevant consents from individuals. DPIAs and DPOs should be put into place for any compliance. Pseudonymization and safeguarding data retention will also help. 

86. How would you design a globally distributed and highly available data pipeline ensuring data consistency?

Replicate datacenters casually across regions employing consistent hashing for fair distribution. Choose an applicable consistency model for the pipeline: strong consistency with Paxos/Raft or eventual consistency to favor availability. Resolve conflicts with the aid of CRDTs or MVCC techniques. 

87. Explain the considerations and strategies for optimizing query performance in a columnar data store.

Optimize queries using column pruning and partition pruning to eliminate I/O for inefficient access. Use efficient compression algorithms and data indexing for faster lookups. A cost-based optimizer selects the optimal execution plan at runtime depending on statistics. 

88. Discuss the challenges and solutions for real-time anomaly detection in high-velocity data streams.

Real-time anomaly detection can be achieved through a scalable machine learning model called Isolation Forests-in windowed stream processing platforms. For out-of-order work events, windowed techniques shall be adopted. Festivals such as Apache Flink are availing advanced state management and event-time processing. 

89. How would you approach designing a Data as a Service (DaaS) platform?

Create a DaaS system using RESTful APIs to access data, align it with comprehensive security systems-perhaps an encryption scheme, authentication, and an authorization level for users-and finally put up governance policies in place for such data access. Create flexibility in data models to deliver various streaming capabilities, whether in real-time streams or batch downloads, and institute capacity. 

90. Explain the complications of cross-cloud data migration and strategies to minimize downtime and data loss.

Data transfer costs incur within transfer operations as well as potential data mismatches. Database replication and change data capture thus do much in reducing transfer downtime. Besides data integrity checks, dedicated networks or transfer appliances might be considered to lessen costs. 

91. Discuss how Quantum Computing might affect data encryption and how to prepare for these changes.

Current cryptography is threatened due to quantum computing. Prepare by researching post-quantum cryptography (e.g., lattice-based, hash-based) and following NIST's standardization process. Invest in quantum-resistant algorithms in preparation for future-proof encryption. 

92. How would you implement a scalable and efficient data versioning system for a large dataset?

Employ an LSM-tree for this system of data versioning to give write efficiency. Delta Lake offers the handling of scalable metadata and ACID transactions in a unified stream-batch processing system. 

93. Discuss the concept of Federated Learning and how it can be used to build privacy-preserving machine learning models.

Federated learning trains models on nonshared data, ensuring that raw data do not leave its original location, thus maintaining privacy. It involves model updates across nodes and their aggregation centrally. This fully satisfies the provisions of the various regulations like GDPR. 

94. Explain how to design a system to guarantee data integrity and accuracy in a Microservices architecture.

By applying a Saga, you guarantee data integrity as it relates to distributed transactions. Each service carries out a local transaction and also publishes events. Event sourcing involves keeping all changes as a sequence of events to provide for possible restoration of the system state at a later time. 

95. Describe how you would set up a data pipeline to handle both batch and stream processing workloads. What technologies would you use, and how would you ensure minimal latency for the streaming data while managing the efficiency of the batch processing tasks?

A unified processing engine, such as Apache Flink or Spark, should be able to process batch and streaming workloads. Streaming processing must be optimized for low latency by in-memory processing and checkpointing, while schedule batch jobs for off-peak hours to fully utilize the productivity. 

96. How would you design a schema evolution strategy for a data lake that receives heterogeneous data sources and formats? What would be your approach to handling breaking schema changes in a production environment without causing downtime or data loss?

Establish a schema registry (e.g., Confluent Schema Registry) for versioning and validation. Use formats like Avro or Parquet that support schema evolution. Ensure that the pipelines implemented will dynamically manage/accept schema changes and will be backward/forward compatible. 

97. Discuss the trade-offs between different data serialization formats such as Avro, Parquet, and JSON in the context of real-time analytics. How would you choose the appropriate format for a given use case, considering factors such as schema evolution, compression, and processing speed?

Avro is the one type of format best used for stream processing application since it is schema-agile and compact. The ideal format for OLAP workloads is the one which is capable of highly optimized storage and has great query performance, which is Parquet. The most inefficient format, yet human-readable, isJSON. Selected on the specific requirement of a case. 

98. How does Machine Learning change Data Engineering?

Machine Learning will change data engineering by allowing systems to learn from a quantity of data, finding patterns, and making autonomous decisions. It would bring data processing and analysis efficiency to better consume and utilize data for decision-making. 

99. Why are Graph Databases good for analyzing data?

Graph databases are very relevant for querying the most complex relationships of data since they quickly answer queries against interconnected parts of the data to understand the structure of networks and the relationships present between nodes. 

100. What's important about processing data in real time?

Real-time processing is mainly very important because it enables applications that require very instant information, such as fraud detection or live analytics, to function. Timeliness in information will thus aid in decision-making and action based on current data and not LED.