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Home / Blog / Interview Questions on Data Engineering / Top 35 Data Pipeline Interview Questions
Sharat Chandra is the head of analytics at 360DigiTMG as well as one of the founders and directors of AiSPRY. With more than 17 years of work experience in the IT sector and Worked as a Data scientist for 14+ years across several industry domains, Sharat Chandra has a wide range of expertise in areas like retail, manufacturing, medical care, etc. With over ten years of expertise as the head trainer at 360DigiTMG, Sharat Chandra has been assisting his pupils in making the move to the IT industry simple. Along with the Oncology team, he made a contribution to the field of LSHC, especially to the field of cancer therapy, which was published in the British magazine of Cancer research magazine.
Table of Content
A real-time data pipeline processes data as it arrives, without delay, enabling immediate data analysis and action. It differs from batch processing, which collects and processes data in large, discrete chunks at scheduled intervals.
Key components include a data ingestion layer (like Kafka or Kinesis), a processing framework (like Apache Storm or Spark Streaming), and a data storage or database system for the processed data.
Ensuring low latency involves optimizing the data ingestion process, using in-memory data processing, minimizing data shuffling, and leveraging distributed computing resources.
Challenges include handling high data velocity, ensuring data quality and consistency, managing resource scalability, and providing fault tolerance and reliable data processing.
Common use cases include fraud detection, real-time analytics and monitoring, instant personalization in web applications, and IoT data processing.
Common technologies include Apache Kafka for data ingestion, Apache Flink, Spark Streaming, or Apache Storm for data processing, and Elasticsearch or Apache Cassandra for data storage.
Backpressure is managed by controlling the data flow rate, using techniques like rate limiting, buffering, or partitioning, and using tools that natively support backpressure management, like Apache Kafka.
Stateful computations in streaming pipelines are managed using state management features in streaming frameworks like Spark Streaming or Flink, which allow for fault-tolerant state keeping across stream processing.
Windowing in stream processing involves dividing the continuous incoming data into discrete chunks or windows, based on time or other criteria, to enable aggregation or analysis over that subset of data.
Data accuracy and consistency are ensured by implementing effective error handling, exactly-once processing semantics, and maintaining data order and integrity through the pipeline.
In real-time processing, ETL/ELT must be designed to handle continuous data flows, requiring more emphasis on speed and scalability and often leading to a shift towards ELT (Extract, Load, Transform) where transformation happens after loading data.
Considerations include ensuring reliable and timely data ingestion, handling various data formats and schemas, and managing the connection to streaming data sources.
Transforming data in real-time involves using stream processing frameworks that can perform operations like filtering, aggregating, and enriching data on-the-fly as it flows through the pipeline.
Loading in real-time pipelines involves persisting processed data to a storage system or database that can handle high-throughput writes and provide quick access for querying and analysis.
Error processing involves setting up robust error handling and retry mechanisms, logging erroneous data for further analysis, and using dead-letter queues to manage unprocessable messages.
Cloud platforms support real-time data pipelines by providing managed services for data ingestion, processing, and storage, such as AWS Kinesis, Azure Stream Analytics, and Google Pub/Sub, which offer scalability and high availability.
Benefits include scalability, cost-effectiveness, ease of deployment and management, built-in security features, and access to a broad ecosystem of integrated services.
Cost optimization involves right-sizing resources, using cost-effective storage, monitoring usage, and selecting appropriate pricing models for cloud services.
Cloud-native tools include AWS Lambda for processing, AWS Kinesis for data streaming, Azure Event Hubs for event ingestion, and Google Dataflow for stream and batch data processing.
They handle security and compliance through encryption, identity and access management, compliance certifications, and offering tools for monitoring and auditing.
Microservices architectures integrate with real-time data pipelines by using event-driven approaches where services communicate through events, often using message brokers like Kafka.
Machine learning can be integrated into real-time data pipelines for predictive analytics, anomaly detection, and automated decision-making based on streaming data.
Managing large-scale data involves distributed processing frameworks, partitioning and sharding data streams, and ensuring high throughput and storage scalability.
Best practices include redundancy, fault-tolerant design, automated recovery mechanisms, real-time monitoring, and regular stress testing.
Event sourcing and CQRS (Command Query Responsibility Segregation) patterns fit well with real-time data pipelines, where changes are captured as immutable events, providing a reliable way to handle data in distributed systems.
Monitoring involves using metrics and logging tools to track throughput, latency, system health, and error rates, often using real-time dashboards and alerts.
Common bottlenecks include data ingestion rates, processing speed, and data storage performance. These are addressed by optimizing code, scaling resources, and fine-tuning configurations.
Scaling involves using auto-scaling features of cloud services, partitioning data streams, and employing distributed processing frameworks that can dynamically allocate resources.
Techniques include using efficient serialization formats like Avro, Protobuf, or JSON, and employing data compression algorithms that balance between compression ratio and speed.
Data quality is managed by implementing real-time validation rules, monitoring for anomalies, and using data cleansing techniques as data flows through the pipeline.
Encrypting data while it's in transit and at rest, putting robust authentication and permission policies in place, and conducting frequent security audits are all necessary to secure real-time data pipelines.
Compliance considerations include adhering to data privacy regulations like GDPR, ensuring data is processed and stored securely, and maintaining audit logs for transparency.
Managing sensitive data involves data masking, tokenization, access controls to restrict sensitive data exposure, and ensuring encryption of data.
Integration involves using APIs, connectors, or middleware to connect real-time pipelines with existing databases, data warehouses, or applications, ensuring compatibility and data consistency.
Ensuring interoperability involves using standard data formats and protocols, adopting open-source technologies with broad support, and using tools that provide connectors to a variety of data sources and sinks.
360DigiTMG - Data Analytics, Data Science Course Training in Chennai
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