Rhombus Interview Questions (Backend Engineer)

What is Apache Spark?

is a fast, in-memory data processing engine with elegant and expressive development APIs to allow data workers to efficiently execute streaming, machine learning or SQL workloads that require fast iterative access to datasets.

What is stream processing?

is a method of processing data continuously as it is generated or received. It allows for real-time or near real-time analysis and action on incoming data streams.

What is batch processing?

is a method of running high-volume, repetitive data jobs where a group of transactions is collected over time, then processed all at once. It's efficient for processing large amounts of data when immediate results are not required.

What is data governance?

is a set of processes, roles, policies, standards, and metrics that ensure the effective and efficient use of information in enabling an organization to achieve its goals. It establishes the processes and responsibilities for data quality, security, and compliance.

What is data masking?

is a technique used to create a structurally similar but inauthentic version of an organization's data. It's used to protect sensitive data while providing a functional substitute for purposes such as software testing and user training.

What is snowflake schema?

is a variation of the star schema where dimension tables are normalized into multiple related tables. This creates a structure that looks like a snowflake, with the fact table at the center and increasingly granular dimension tables branching out.

What is PySpark?

is the Python API for Apache Spark. It allows you to write Spark applications using Python, combining the simplicity of Python with the power of Spark for distributed data processing.

What is normalization in database design?

is the process of organizing data in a database to reduce redundancy and improve data integrity. It involves breaking down larger tables into smaller, more focused tables and establishing relationships between them.

What Data orchestration platform have you used?

APACHE AIRFLOW

What is your favorite ETL tool you have used.

Apache Spark, Apache Kafka, Data bricks Delta Live tabels

What are the three stages of building a model in machine learning?

Building a model in machine learning involves three key stages: training, validation, and testing. During the training stage, the model learns patterns and relationships in the data by using the training dataset to adjust its parameters. In the validation stage, the model is evaluated on a separate validation dataset to fine-tune hyperparameters, optimize its performance, and prevent overfitting. Finally, in the testing stage, the model is assessed on an unseen test dataset to measure its performance and ensure it generalizes well to new data.

What are the three main types of data models?

Conceptual data model: High-level view of data structures and relationships Logical data model: Detailed view of data structures, independent of any specific database management system Physical data model: Representation of the data model as implemented in a specific database system

What is the difference between a data lake and a data warehouse?

Data warehouses store structured data, while data lakes can store structured, semi-structured, and unstructured data Data warehouses are optimized for analysis, while data lakes serve as a repository for raw data

How will you handle an imbalanced dataset?

Handling an imbalanced dataset involves several strategies to improve model performance and ensure fairness in predictions. First, you can resample the dataset, either by oversampling the minority class (e.g., using SMOTE) or undersampling the majority class to balance the class distribution. Second, you can apply class weighting, where the model assigns higher importance to the minority class during training. Third, you can use specialized algorithms like ensemble methods (e.g., Random Forests or XGBoost) that handle imbalanced data well. Additionally, evaluating the model with metrics like precision, recall, F1-score, and AUC-ROC is critical instead of relying solely on accuracy, as these metrics provide a clearer picture of performance for imbalanced datasets.

How do you handle data skew in distributed processing systems?

Identifying and analyzing skewed keys Implementing salting or hashing techniques to distribute data more evenly Using broadcast joins for small datasets Adjusting partition sizes or using custom partitioners Implementing two-phase aggregation for skewed aggregations Considering alternative data models or schema designs

How do you ensure data quality in your projects?

Implementing data validation checks at ingestion Using data profiling tools to understand data characteristics Establishing clear data quality metrics and monitoring them Implementing data cleansing processes Conducting regular data audits Establishing a data governance framework

How do you ensure data consistency in distributed systems?

Implementing strong consistency models where necessary Using eventual consistency for improved performance in certain scenarios Implementing distributed transactions when needed Using techniques like two-phase commit or saga pattern for complex operations Implementing idempotent operations to handle duplicate requests Designing for conflict resolution in multi-master systems

What strategies do you use for optimizing query performance in large datasets?

Proper indexing of frequently queried columns Partitioning large tables Using materialized views for complex, frequently-run queries Query optimization and rewriting Implementing caching mechanisms Using columnar storage formats for analytical workloads Leveraging distributed computing for large-scale data processing

What is a data warehouse?

is a centralized repository that stores large amounts of structured data from various sources in an organization. It is designed for query and analysis rather than for transaction processing.

What is the slowly changing dimension (SCD)?

is a concept in data warehousing that describes how to handle changes to dimension data over time. Type 1: Overwrite the old value Type 2: Create a new row with the changed data Type 3: Add a new column to track changes

What is the Lambda architecture?

is a data processing architecture designed to handle massive quantities of data by taking advantage of both batch and stream processing methods. It consists of three layers: Batch layer: Manages the master dataset and pre-computes batch views Speed layer: Handles real-time data processing Serving layer: Responds to queries by combining results from batch and speed layers

What is star schema?

is a data warehouse schema where a central fact table is surrounded by dimension tables. It's called a star schema because the diagram resembles a star, with the fact table at the center and dimension tables as points.

What is Apache Kafka?

is a distributed streaming platform that allows for publishing and subscribing to streams of records, storing streams of records in a fault-tolerant way, and processing streams of records as they occur.

What are the "training Set" and "test Set" in a Machine Learning Model? How much data will you allocate for your training, validation, and test sets?

In machine learning, the dataset is split into three parts: the training set, validation set, and test set. The training set, typically 60-80% of the data, is used to train the model. The validation set, around 10-20%, helps tune hyperparameters and prevent overfitting. The test set, also 10-20%, evaluates the model's final performance on unseen data. The exact split depends on the dataset size and the complexity of the problem.

Explain the ETL process.

It is a process used to collect data from various sources, transform it to fit operational needs, and load it into the end target, usually a data warehouse. The steps are: Extract: Retrieve data from source systems Transform: Clean, validate, and convert the data into a suitable format Load: Insert the transformed data into the target system Tools include : Apache Nifi, AWS GLUE, Apache Spark, Databricks

How do you approach data pipeline testing?

Unit testing individual components Integration testing to ensure components work together End-to-end testing of the entire pipeline Data validation testing to ensure data integrity Performance testing under various load conditions Fault injection testing to verify error handling Regression testing after making changes

How would you design a system to handle real-time streaming data?

Using a distributed streaming platform like Apache Kafka or Amazon Kinesis Implementing stream processing with tools like Apache Flink or Spark Streaming Ensuring low-latency data ingestion and processing Designing for fault tolerance and scalability Implementing proper error handling and data validation Considering data storage for both raw and processed data

How do you handle schema evolution in data pipelines?

Using schema-on-read formats like Parquet or Avro Implementing backward and forward compatibility in schema designs Versioning schemas and maintaining compatibility between versions Using schema registries for centralized schema management Implementing data migration strategies for major schema changes Testing schema changes thoroughly before deployment

What is your experience with data versioning and how do you implement it?

Using version control systems for code and configuration files Implementing slowly changing dimensions in data warehouses Using data lake technologies that support versioning (e.g., Delta Lake) Maintaining metadata about dataset versions Implementing a robust backup and restore strategy