Partitioning breaks large datasets into smaller segments based on keys like date, region, or category. This significantly reduces the amount of data scanned during queries.

Distributed systems like Spark, Hive, and BigQuery rely heavily on partitioning for optimization. Proper partition design helps lower storage costs and speeds up job execution. It also improves parallelism across compute nodes.

Linear regression finds the optimal line by minimizing the sum of squared residuals between predictions and true values. The slope and intercept are calculated using analytical formulas or optimization techniques.

The model assumes a linear relationship between input features and output. Residual plots help evaluate goodness of fit. Regularization variants like Ridge and Lasso improve performance on noisy data.

Kafka streams data through producers that write events into topics, which are divided into partitions for parallelism. Consumers read records at their own pace while brokers guarantee durability.

This decoupled, high-throughput architecture supports real-time pipelines. Kafka integrates with Spark Streaming and Flink for real-time analytics. Its log-based design ensures ordered, persistent data streams.

ETL handles heavy transformations before loading, while ELT leverages modern cloud compute power. ELT is now preferred for scalable analytics workflows.

Warehouses deliver clean, structured data for analytics, while lakes store raw, flexible data for exploration and machine learning.

. What are the differences between batch processing and stream processing?

Answer:

Batch suits historical analysis, while stream processing powers dashboards, alerts, and live applications.

SQL supports strong consistency, while NoSQL excels in distributed, high-volume environments.

Data modeling defines the structure, relationships, and rules for organizing data. A strong model ensures consistency across systems and reduces redundancy. It also improves query performance and data quality. Good modeling supports scalable analytics as datasets grow. Star and snowflake schemas are common in data warehouses.

Data lineage tracks how data moves through pipelines—from ingestion to transformation to storage. It helps debug issues by showing exactly where data was modified. Lineage improves transparency in regulated industries. Tools like Apache Atlas and Collibra automate lineage tracking. Understanding lineage ensures trust in analytics outcomes.

Schema evolution allows datasets to change structure without breaking existing pipelines. Formats like Avro, Parquet, and ORC support backward and forward compatibility. This flexibility is essential for continuously changing business requirements. Schema evolution enables safe updates to fields, types, and metadata. It also simplifies long-term data storage in data lakes.

Spark uses in-memory computation to accelerate workloads compared to disk-based systems. Its DAG scheduler optimizes execution paths efficiently. Catalyst optimizer improves SQL query execution. Tungsten engine boosts performance via efficient memory and CPU usage. Together, these features make Spark ideal for large datasets and ML workflows.

Data pipelines consist of multiple dependent tasks requiring coordination. Orchestration tools ensure jobs run in the correct order and handle failures automatically. They also provide scheduling, monitoring, and retry logic. Tools like Airflow, Prefect, and Luigi automate complex workflows. Orchestration ensures reliability and efficiency in production environments.

Columnar formats like Parquet and ORC store data by columns instead of rows. This drastically reduces storage size due to compression. Columnar storage improves analytical query performance by scanning only relevant columns. It integrates well with Spark, Hive, and cloud warehouses. It is ideal for read-heavy workloads in data science.

Sharding splits a dataset horizontally across multiple machines to improve scalability. It allows distributed databases to handle high write and read loads. Proper shard keys ensure even data distribution. Sharding is common in NoSQL systems like MongoDB and Cassandra. It supports large-scale, high-traffic applications.

Metadata provides information about pipeline tasks, sources, schemas, and runtime statistics. Monitoring detects performance bottlenecks and failures quickly. Together, they ensure reliability and transparency. Proper instrumentation helps debug issues and optimize resource usage. Platforms like Airflow and Datadog automate workflow observability.

Parquet compresses data and stores it in a binary, columnar layout. It supports predicate pushdown, reducing scanned data and speeding up queries. Unlike CSV, Parquet preserves schema and data types. It integrates seamlessly with Spark, Hive, and cloud storage. Overall, Parquet dramatically improves analytics efficiency.

Deduplication removes repeated records to ensure accuracy and reduce storage. Duplicate data skews analytics and increases processing time. Techniques include hashing, clustering, and rule-based checks. Deduplication is crucial for merged data sources or user-generated inputs. Clean datasets improve model training and reporting quality.

Message queues decouple producers and consumers, improving scalability. They buffer incoming data and ensure reliable delivery. Systems like Kafka, RabbitMQ, and AWS SQS handle high-volume event ingestion. Queues enable asynchronous processing and microservice communication. They form the backbone of modern event-driven architectures.

Caching stores frequently accessed data in high-speed memory. This significantly reduces repeated computation and I/O operations.

Distributed caches like Redis and Memcached accelerate real-time pipelines. Caching is especially useful in API systems and ML feature retrieval. It ensures low latency and improved scalability.