Spark Online Test

The Spark Test makes use of scenario-based questions to test for on-the-job skills as opposed to theoretical knowledge, ensuring that candidates who do well on this screening test have the relavant skills. The questions are designed to covered following on-the-job aspects:

• Fundamentals of Spark Core
• Developing and running Spark jobs in Java, Scala, and Python
• Understanding Spark Resilient Distributed Datasets (RDD)
• Data processing with Spark SQL
• Working with Dataframes and Datasets in Spark
• Utilizing Spark Streaming for real-time data processing
• Running Spark on a cluster
• Implementing iterative and multi-stage algorithms in Spark
• Tuning and troubleshooting Spark jobs in a cluster
• Performing Graph/Network analysis with GraphX library in Spark

Once the test is sent to a candidate, the candidate receives a link in email to take the test. For each candidate, you will receive a detailed report with skills breakdown and benchmarks to shortlist the top candidates from your pool.

• Fundamentals of Spark Core

  Understanding Spark Core involves knowledge of the basic building blocks and execution model of Apache Spark, such as RDDs, transformations, and actions. This skill is necessary to develop efficient and scalable Spark applications.

• Developing and running Spark jobs (Java; Scala; Python)

  Developing and running Spark jobs requires proficiency in programming languages like Java, Scala, or Python. This skill is crucial to write Spark applications using Spark APIs, perform data processing tasks, and leverage the power of Spark's distributed computing capabilities.

• Spark Resilient Distributed Datasets (RDD)

  Spark RDDs are fundamental data structures in Spark that allow for distributed data processing and fault tolerance. Understanding RDDs is essential for efficient data manipulation, transformation, and parallel computing in Spark.

• Data processing with Spark SQL

  Spark SQL is a module in Spark that provides a programming interface for querying structured and semi-structured data using SQL-like syntax. This skill is important to analyze and process structured data using SQL operations and leverage the optimizations provided by Spark SQL's query engine.

• Dataframes and Datasets

  Dataframes and Datasets are higher-level abstractions built on top of RDDs in Spark. They provide a more expressive and efficient way to work with structured and unstructured data. Understanding Dataframes and Datasets is crucial for performing data manipulations, transformations, and aggregations efficiently in Spark.

• Spark Streaming to process real-time data

  Spark Streaming is a scalable and fault-tolerant stream processing library in Spark that allows for real-time data processing. This skill is important to handle continuous streams of data and perform real-time analytics, enabling applications to react to data changes in near real-time.

• Running Spark on a cluster

  Running Spark on a cluster involves configuring and deploying Spark applications across a distributed cluster infrastructure. This skill is necessary to take advantage of Spark's distributed computing capabilities and ensure optimal performance and scalability.

• Implementing iterative and multi-stage algorithms

  Implementing iterative and multi-stage algorithms in Spark involves designing and optimizing algorithms that require multiple iterations or stages to achieve the desired output. This skill is important for tasks like machine learning and graph processing that often involve complex iterative and multi-stage computations.

• Tuning and troubleshooting Spark jobs in a cluster

  Tuning and troubleshooting Spark jobs in a cluster requires expertise in identifying and resolving performance issues, optimizing resource utilization, and ensuring fault tolerance. This skill is crucial to maximize the efficiency and reliability of Spark applications running on a distributed cluster.

• Graph/ Network analysis with GraphX library

  GraphX is a graph computation library in Spark that provides an API for graph processing and analysis. Understanding GraphX is important for tasks like social network analysis, recommendation systems, and fraud detection that involve analyzing relationships and patterns in graph data.

• Migrating data from data sources/ databases

  Migrating data from data sources or databases to Spark involves understanding various data ingestion techniques, such as batch processing, streaming, and data connectors. This skill is necessary to efficiently transfer and process data from external sources in Spark for further analysis and computation.

• Full list of covered topics

  The actual topics of the questions in the final test will depend on your job description and requirements. However, here's a list of topics you can expect the questions for Spark Test to be based on.

  Spark RDD

  Spark DataFrame

  Spark Dataset

  Spark SQL

  Spark Streaming

  Spark GraphX

  Spark Cluster

  Spark Graph and Network Analysis

  Spark Iterative Algorithms

  Spark Multi-stage Algorithms

  Spark Job Tuning

  Spark Job Troubleshooting

  Spark Data Migration

  Spark Core Fundamentals

  Java Spark Development

  Scala Spark Development

  Python Spark Development

  Data Processing in Spark

  Real-time Data Processing in Spark

  Spark Architecture

  Handling Exceptions and Errors in Spark

  Spark Data Manipulation

  Spark Data Aggregation

  Spark Data Filtering

  Spark Data Transformation

  Spark Data Visualization

  Spark Data Joins

  Spark Data Partitioning

  Spark Data Caching

  Spark Data Serialization

  Spark Data Compression

  Spark Data Sources

  Spark Data Loading

  Spark Data Saving

  Spark Data Exploration

  Spark Data Preprocessing

  Spark Data Analytics

  Spark Data Mining

  Spark Data Quality

  Spark Data Integration

  Spark Data Streaming

  Spark Data Pipelines

  Spark Data Storage

  Spark Data Security

  Spark Data Access Control

  Spark Data Backup

  Spark Data Recovery

  Spark Data Replication

  Spark Data Compression

  Spark Data Encryption

  Spark Data Schema

  Spark Data Serialization

  Spark Data Indexing

  Spark Data Visualization

  Spark Data Benchmarking

  Spark Machine Learning

  Spark Deep Learning

  Spark Neural Networks

  Spark Graph Algorithms

  Spark Social Network Analysis

  Spark Community Detection

  Spark Clustering

  Spark Classification

  Spark Regression

  Spark Anomaly Detection

  Spark Recommendation Systems

  Spark Sentiment Analysis

  Spark Natural Language Processing

  Spark Geospatial Analysis

  Spark Time Series Analysis

  Spark Collaborative Filtering

  Spark Dimensionality Reduction

  Spark Model Evaluation

  Spark Feature Engineering

  Spark Feature Selection

  Spark Hyperparameter Tuning

  Spark Model Deployment

  Spark Model Monitoring

  Spark Model Interpretability

• Spark Developer
• Software Developer - Spark
• Big Data Engineer
• Senior Spark Developer
• Scala Big Data Developer
• Senior Big Data Engineer
• Spark Engineer

For intermediate/ experienced candidates, we customize the assessment questions to include advanced topics and increase the difficulty level of the questions. This might include adding questions on topics like

• Migrating data from various data sources/databases
• Working with Spark MLlib for machine learning tasks
• Optimizing Spark performance using caching and persistence
• Using Spark for natural language processing (NLP) tasks
• Implementing Spark for real-time analytics
• Understanding and managing Spark Executors and Workers
• Utilizing Spark for large-scale data processing
• Implementing Spark for real-time data visualization
• Integrating Spark with other Big Data technologies like Hadoop and Cassandra
• Implementing Spark on cloud platforms for scalability and flexibility