In this article, we have compiled a list of 30+ top data lake interview questions and answers. These questions cover a broad range of topics, from the basics of data lake architecture to more advanced topics like data governance and security. Data Lakes are becoming increasingly popular in enterprise data management strategy. Companies of all sizes and across industries are investing in this technology and as a result, there is a growing demand for professionals with data lake knowledge. By studying these questions, you’ll be better equipped to showcase your Data lakes related knowledge. Happy Learning!
Top Data Lake Interview Questions
- What are the uses of a data lake?
- What type of data can be stored in a data lake?
- What is the difference between a data lake and a data warehouse?
- What skills are required to set up/design a data lake?
- Describe common pitfalls while implementing a data lake solution.
- What are the typical steps in the data lake design & implementation journey?
- How does a data lake give the business a competitive advantage?
- What sources of data can be used by a data lake?
Easy/Fresher Level Data Lake Interview Questions
Q1. What is a data lake?
The data lake is an enterprise data storage system that helps to save, process, and analyze large volumes of data within a single platform securely and cost-effectively. A data lake stores different forms of data coming from various source systems through a common framework into a centralized storage repository and makes it easily available for querying and analytics.
Q2. What are some commercial data lake tools/products available?
Many different data lake products are available in the market. Some of the popular tools include:
Q3. What are the uses of a data lake?
The primary usage of a data lake is to store vast amounts of meaningful data. It allows applying various analytics techniques to identify interesting facts about the data that can help in decision-making. For example, data lakes provide versatile dashboards to users for better visualization of the data and track any changes.
Q4. Which real-world industries are using data lakes?
Because of the valuable features and functionality of the data lake, it can be adopted by nearly every industry. Some of the prominent businesses that can use data lakes are:
- The media and entertainment industry can use data lakes to get recommendations about the customers’ preferences to improve their services and income.
- Mobile and telecommunication services can use data lakes to predict and minimize customer turnover.
- The finance industry can use data lakes to process real-time data and reduce risks. Banks around the globe are already using data lakes to increase data utility and satisfy customer requirements.
- The retail sector can monitor the products in demand and serve the right target market using data lake analytics.
Q5. Does a data lake allow both structured and unstructured data?
Yes, a data lake allows structured, unstructured, and even semi-structured data. It requires no pre-processing of data which is otherwise time-consuming. All the structured data inserted from database tables, semi-structured data in the form of log files, or unstructured data like binary files are stored in a single central storage unit.
Related Question: Data Architecture – Can you explain the difference between structured, semi-structured, and unstructured data?
Q6. What are the advantages of using a data lake?
A data lake provides several benefits to the users, such as:
- It provides quick access to data as all resources are stored in a central unit with no design limitations.
- It offers low-cost scalability and storage repositories because it is built on inexpensive commodity servers.
- It adapts to changes rapidly due to its flexible architecture and no-hard-bound design specifications.
- It allows data from varying sources in several different formats and types.
- It provides instant results of processing using modern tools and technologies.
Q7. What are the disadvantages of using a data lake?
A few of the flaws in data lakes have been listed below:
- Data lakes may suffer from redundancy and cluttering due to unstructured data. With no checks or filters, the same data can get stored repeatedly.
- Data lakes have high risks of security breaches which affects the data quality. Inadequate or ineffective security protocols can compromise data privacy.
- Data lakes follow no proper data structure, making it difficult to track changes or addition in records. Searching for a particular instance and deleting an entry can be problematic.
Q8. Should the entire organization have access to the data lake?
It’s not necessary to grant data lake access to the entire organization without need. The concerned resources should be given access to control the data and track the activities. Implementing proper and effective access rights is a critical part of implementing an effective data lake strategy in a data lake as unrestricted access can result in illegal activities or data tampering.
Q9. Does a data lake require modifying data before it can be added?
There are no hard restrictions or rules to be followed for adding data to a data lake. It accepts raw or unprocessed data without changing the format.
Q10. What type of data can be stored in a data lake?
Data in almost any format, such as images, videos, pdf documents, email text, CSV or audio files, etc can be stored in a data lake which can later be converted into another format.
Medium/Intermediate Data Lake Interview Questions for Experienced
Q11. What is the difference between a data lake and a data warehouse?
Data lake and data warehouse are both used for storing data but differ in properties. Data lakes accept unprocessed data but data warehouses need pre-processed or semi-structured data. Data stored within the data lake can be quickly accessed and changed without any limitations. Whereas a data warehouse doesn’t provide instant access and flexibility to changing data. Data lakes are easier to scale and cheaper to implement, but data warehouses have expensive implementation and maintenance. For a more in-depth explanation, see Data Lake versus Data Warehouse.
Related Reading: Data Warehouse Interview Questions
Q12. What are the pros and cons of a cloud-based data lake?
A data lake that resides in the cloud has both benefits and drawbacks. These pros and cons are listed below:
Pros of Cloud-based Data Lake
- With a cloud data lake, users are charged for only the services they wish to use.
- Cloud data lake provides options to activate or disable services on the go, such as limitless scaling capability.
- Cloud data lake facilitates periodic data backup to prevent data loss.
- Users can observe their activities and expenses clearly.
Cons of Cloud-based Data Lake
- Migration from a cloud data lake to another platform can be difficult due to the complicated structure.
- Unstructured data storage can raise data governance issues.
Q13. How is a data lake different from a relational database?
A data lake is often considered a database because of the storage feature similarity. Both have differences in functionalities and structure. A database stores fresh data coming from one application, whereas a data lake keeps both old and latest data from multiple sources. Moreover, a data lake allows data in any form, but unstructured data is not allowed in a relational database.
Q14. What sources of data can be used by a data lake?
Data from different sources, such as log files, mobile applications, social media posts, IoT device sensors, etc can be used in data lakes. Aberdeen’s 2017 Big Data survey shows that users prefer handling complex data sources.
Q15. Why is it important to use a data lake?
The current data explosion and growing organizational needs can be managed with a platform that provides adequate features. The data lake is a one-stop solution built to handle large amounts of data without any restrictions or limitations. It can help organizations/businesses to:
- Boost their performance and productivity
- Build applications
- Explore and monitor data
- Maintain data authenticity
Q16. How much storage capacity can a data lake provide?
Data lakes are known for offering data storage for several petabytes of data. Typically, the data lake providers allow scale-as-you-go to add as much storage as users want for their requirements. According to the 2018 Big Data Trends and Challenges Report, approximately 44 percent of companies were using data lakes, offering 100 terabytes of data capacity on average. Different data lake vendors/companies offer different storage capacities, which can be set according to organizational needs.
Q17. Which data management systems can be used with a data lake?
Data management systems with flexible schema, supporting different data structures (structured and semi-structured) can be used with a data lake.
Q18. What is the Extract and Load or “EL” process in a data lake?
The process of extracting or accessing data from its source/origin and loading it into a storage unit is called the “EL” or Extract and Load process. The data can be accessed in stages or all at once. After extracting complete data, it can be loaded into the data lake. The revised form of this concept is known as “ELT”, which stands for Extract, Load, and Transform where data is transformed in the end.
Related Reading: Introduction to AWS Glue
Q19. What is the cost of setting up a data lake?
The cost of setting up a data lake primarily depends on the storage capacity and service rates, which vary between vendors. The research article provides more detailed information about the pricing strategies of different data lake companies. Cost Modelling for Data Lakes by Amazon Web Services discusses the factors affecting the cost and guides about optimizing the overall pricing.
Expert Level Data Lake Interview Questions for Experienced
Q20. What skills are required to set up/design a data lake?
The skills needed to build or manage data lakes include:
- Data Architecture: It includes designing the data lake architecture and data flows from enterprise systems and applications into the data lake.
- Data Engineering: It involves preparing, designing, developing, and deploying data from the origin to the data lake.
- Data Validation: It requires ensuring the accuracy and authenticity of data to be added to the data lake.
- Data Science: It includes data examination to identify patterns and generate useful insights.
- Data Analytics: It involves the ability to define organizational goals and use relevant analytics tools.
Q21. Which modern technologies are supported/featured/adopted by a data lake?
The data lake is composed of the following modern features/technologies:
- It provides data engineering or processing using SQL, Python, R, Apache Spark, etc.
- It enables visualization of the data insights with Tableau and Power BI tools.
- It allows using machine learning models via simple Jupyter notebooks for processing data.
Q22. How can a data lake help in analysis?
One of data lake’s major contributions is effective data analysis. It allows a combination of tools to analyze data, such as SQL, Apache Spark, Power BI, Python, R, Tableau, etc. The enterprise log data is connected to the analytics tools to find useful insights.
Q23. How to make a data lake secure/improve the security of a data lake?
Some practices that can help improve data lake security are:
- Restrict unnecessary access to the data records by setting access controls on column and row levels.
- Implement security protocols and measures before adding sensitive data, such as enterprise firewalls.
- Add a host-based security layer to prevent external attacks and get real-time alerts with a host-intrusion detection system.
- Run security scans frequently to monitor data late health and environment.
Data security is a critical aspect of data lake governance. Understanding and implementing the right data security standards is crucial for protecting sensitive information within a data lake. Check out our comprehensive guide on the top data security standards every data professional must know, including those relevant to data lakes.
Q24. What does the “schema-on-read” principle mean in a data lake?
Traditional databases and SQL engines work with structured tables having pre-defined designs. Modern data sources such as logs, mobile applications, sensors, and IoT usually produce semi-structured data in large quantities. The semi-structured data is difficult to process in the database due to the lack of a proper structure or schema. Inability to store and process data results in the underutilization of data. Data lakes follow a “schema-on-read” principle allowing data with no pre-defined schema to be stored. With “schema-on-read”, the structure of data can be defined during processing or analysis.
Q25. Describe a typical data lake architecture.
A typical data lake comprises of the following 4 layers: The ingestion layer, the distillation layer, the processing layer, and the insights layer.
- Ingestion layer: The function of this layer is to add raw data to the data lake.
- Distillation layer: In the distillation layer, raw data is converted into structured data set to be saved as files or tables.
- Processing layer: The data processing takes place in the processing layer by using analysis tools and running queries.
- Insights layer: The results of data analysis are forwarded to the dashboard in this layer.
Q26. What causes a data lake to turn into a data swamp?
A data swamp is unmanaged data storage that becomes less useful for the users. Data swamps can occur due to inadequate data quality and ineffective security protocols. A data lake transforms into a data swamp when organizations don’t define clear guidelines and keep on dumping unrelated or unwanted data. Swamps lead to difficulty in accessing and analyzing data.
Q27. Describe common data lake antipatterns.
Common data lake antipatterns are discussed below:
- Storage Console Dependency: Developers often spend extra time reviewing the internal metrics within the console data structure, which steals their productivity. Automating the report generation for such metrics is one solution.
- Physical Duplication of Files: Some users tend to make copies of files containing the same dataset physically to maintain data. It wastes time which can be saved by running a single shell command for the same purpose.
- Complex Data Processing: Implementing a single static function for every data element will have to be updated or executed on every new data entry. The simple remedy is to create separate functions for every dataset.
Q28. Describe common pitfalls while implementing a data lake solution.
Some of the common pitfalls while implementing a data lake solution are:
- Excessive or Inadequate Data Governance: Some companies follow strict data governance in a data lake by applying excessive restrictions on viewing, accessing, and modifying data. This may lead to a situation where no member can access or view data, rendering it useless. In contrast, not having enough governance results in a lack of tools and regulations to handle data. This affects overall data quality losing the organization’s trust and rendering it useless once again.
- Inflexibility of Architecture: While building a data lake, organizations choose an inflexible architecture to meet their needs. To manage storage costs, organizations expand the storage capacity by adding basic units slowly and periodically. As the data keeps growing, the demands rise which leads to purchasing a costly high, performing server. In the long run, maintaining the data needs and upgrading the servers for a large setup gets complicated.
- Inappropriate Data Handling: Organizations often set up data lakes to dump their data without any planning or categorization. Storing anything and everything makes it hard for analysts to find useful data and derive patterns. Data scientists have to spend more time digging data and applying transformations.
Q29. What are the typical steps in the data lake design & implementation journey?
The typical steps in the data lake design and implementation are:
- Setting up storage
- Moving data to the data lake storage
- Cleaning, preparing, and processing data
- Configuring security settings and applying privacy policies
- Presenting data for analytics
- Visualizing data
Q30. How does a data lake give the business a competitive advantage?
The data lake is super effective in boosting performance and managing demands of all the business domains such as marketing, sales, production, operations, etc. Businesses get a competitive advantage with data lakes because:
- Most businesses manage unstructured data: Forbes survey states that about 95% of businesses rely on unstructured data. For instance, a company dealing with appointments or reservations uses (unstructured) text field data. In contrast, social media posts on the company’s online channels or images/videos used as instructions by workers to perform their tasks are all unstructured data. Data lakes easily allow storing and processing of unstructured data, which is highly beneficial for businesses.
- Businesses prefer cost-effective solutions: One of the objectives of successful entrepreneurs is to make low-risk, high-reward investments. As data lakes allow low-cost storage capacity and scalability, business firms can upscale the services when needed by paying for what they get.
- Marketers and Strategists need real-time data analysis: Understanding the customer needs and interests is the key to success for any firm. Powerful real-time analysis in data lakes help business gain insights into the current trends and changing demands. Careful analysis of the results can help businesses in decision-making to reap maximum profits and amplify revenue.
Q31. Describe the pros and cons of leveraging a data lake PaaS (platform-as-a-service. E.g., data bricks, snowflake) vs. IaaS (Infrastructure as service. E.g., AWS).
Leveraging a data lake, Paas or Iaas, can have its own benefits and disadvantages. The pros and cons of leveraging data lake PaaS are given below:
|Pros of leveraging data lake PaaS (platform-as-a-service)||Cons of leveraging data lake PaaS (platform-as-a-service)|
|Less complex infrastructure||Weak security protocols|
|Open selection of tools|
The pros and cons of leveraging data lake IaaS are given below:
|Pros of leveraging data lake IaaS (infrastructure-as-a-service)||Cons of leveraging data lake IaaS (infrastructure-as-a-service)|
|Unlimited data storage|
Q32. What are the access patterns to retrieve data from a data lake?
Data in the data lake is valuable only if it is easily accessible to the downstream applications as and when required. Some frequently used patterns to access data from the data lake are interactive queries, CDC subscriptions, streaming, batch processing and API access.
Q33. List some of the tools and managed services that can be used to build and maintain data lakes using modern data pipelines
Some of the managed services to build cloud-native, modern, data pipelines are:
- AWS Glue (Related reading: AWS Glue interview questions and answers)
- Amazon Managed Workflows for Apache Airflow (MWAA)
- Azure Data Factory
- Google Cloud Composer (built on Apache Airflow, similar to MWAA)
- Talend Open Studio
- Matillion ETL
Q34. What are the five pillars of data lake governance?
Five pillars of data lake governance are: Data security and accessibility, data quality, data resiliency, organizational agility, cost management and optimization.
Organizations can get the most out of their data lake investments by focusing on these five pillars while setting up their data lake governance framework.
Q35. How does data lake infrastructure help in predictive analytics?
Data lake infrastructure, by providing ready access to large datasets in a centralized repository, significantly eases the process of predictive analytics. It enables data analysts to transform, explore, and model data efficiently for future trend prediction. Data stored in its raw form can be processed and transformed as required specifically for predictive analytics, thereby enhancing the overall efficiency of the process.
Q36. How do the data warehouse and data lakehouse models compare and contrast?
A data lakehouse model essentially combines data warehouses and data lakes’ features to provide a single data source. While data warehouses offer processed and structured data ideal for business intelligence, data lakes store raw data of all types, providing more flexibility for data analytics. A data lakehouse model will manage structured and unstructured data and can run both BI and advanced analytic workloads.
Q37. How do organizational reporting systems benefit from data lakes?
Organizational reporting systems benefit immensely from data lakes because of their data storage and processing capabilities. Having all their data consolidated in one place allows organizations access to real-time data, which is critical for timely decision-making. This can significantly enhance the quality of reports and enable the business to respond more swiftly to dynamic market conditions.
- For a practical application of these fundamentals in AWS environments, check out our guide on Top 10 S3 Data Lake Best Practices.
- Data Lake vs Data Warehouse
- What is a Data Scientist?
- Introduction to AWS Glue
- Data Lake Access Patterns to Get the Most out of your Data Lake
- Interview Preparation: AWS Glue Interview Questions
- Interview Preparation: Data Governance Interview Questions
- Interview Preparation: Collibra Interview Questions
- Interview Preparation: Data Warehouse Interview Questions