10+ Data Analytics Engineer Interview Questions and Answers

Batch processing handles large data sets at once, while streaming processes data in real-time for immediate insights.

• Batch Processing: Suitable for large volumes of data; e.g., monthly sales reports.

• Streaming: Ideal for real-time data; e.g., monitoring patient vitals continuously.

• Batch Architecture: Involves ETL processes, data lakes, and scheduled jobs.

• Streaming Architecture: Utilizes message queues, stream processing frameworks like Apache Kafka or Spark Streaming.

• Data Stor...read more

Mapping source to target involves understanding data flow and designing data marts for efficient analytics.

• Identify source systems: Understand where the data originates, e.g., CRM, ERP.

• Define target schema: Design the data mart schema, e.g., star or snowflake schema.

• Data transformation: Apply ETL processes to clean and transform data for analysis.

• Data modeling: Create logical and physical models to represent data relationships.

• Testing and validation: Ensure data integrity and...read more

Designing data models for a modern data warehouse involves structuring data for efficient querying and analysis.

• Identify business requirements: Understand the key metrics and KPIs needed by stakeholders.

• Choose a modeling technique: Use star schema for simplicity or snowflake schema for normalization.

• Define fact and dimension tables: Fact tables store quantitative data, while dimension tables provide context (e.g., sales data as facts and product details as dimensions).

• Impleme...read more

A window function in SQL performs calculations across a set of table rows related to the current row.

• Window functions allow you to perform calculations like running totals, averages, and rankings without collapsing rows.

• They are defined using the OVER() clause, which specifies the partitioning and ordering of the data.

• Example: SELECT employee_id, salary, SUM(salary) OVER (PARTITION BY department_id) AS department_total FROM employees;

• Common window functions include ROW_NUMBER...read more

An AI agent is a software entity that perceives its environment and takes actions to achieve specific goals autonomously.

• AI agents can be reactive, responding to changes in their environment, like a self-driving car adjusting to traffic.

• They can also be proactive, planning actions based on predictions, such as virtual assistants scheduling meetings.

• AI agents utilize machine learning algorithms to improve their performance over time, like recommendation systems on streaming pl...read more

PCA (Principal Component Analysis) is a statistical technique used for dimensionality reduction and data visualization.

• PCA transforms data into a new coordinate system, where the greatest variance lies on the first axis.

• It helps in reducing the number of features while retaining the essential information.

• For example, in image processing, PCA can reduce the number of pixels while preserving the image quality.

• PCA is widely used in exploratory data analysis and for making predic...read more

Python code to check if a string is a palindrome

• Use string slicing to reverse the input string and compare it with the original string

• Ignore spaces and punctuation when checking for palindrome

• Convert the input string to lowercase for case-insensitive comparison

VLOOKUP is a powerful Excel function used to search for a value in a table and return related data from another column.

• VLOOKUP syntax: VLOOKUP(lookup_value, table_array, col_index_num, [range_lookup]).

• Example: =VLOOKUP(A2, B2:D10, 3, FALSE) looks for A2 in the first column of B2:D10 and returns the value from the third column.

• Range_lookup can be TRUE (approximate match) or FALSE (exact match).

• Common errors include #N/A (value not found) and #REF! (invalid column index).

• VLOOKU...read more

Excel is a powerful spreadsheet tool used for data analysis, visualization, and management.

• Excel functions: Use functions like SUM(), AVERAGE(), and VLOOKUP() for calculations and data retrieval.

• Data visualization: Create charts (e.g., bar, line, pie) to visually represent data trends.

• Pivot tables: Summarize large datasets quickly by creating pivot tables for analysis.

• Conditional formatting: Highlight important data points based on specific criteria for better insights.

• Data f...read more