Hinzinfotech

Below are 20 PySpark interview questions that appear in almost every data engineering interview — along with concise, practical answers you can use to demonstrate mastery. 1. Difference Between RDD, DataFrame, and Dataset 👉 In Python, we mostly use DataFrames. 2. Lazy Evaluation Spark doesn’t execute transformations immediately. It builds a logical DAG (Directed Acyclic […]

In the modern data ecosystem, automation is key to building reliable, efficient, and scalable data pipelines. As data volumes and frequency of updates increase, manually managing ETL (Extract, Transform, Load) jobs becomes inefficient. That’s where Snowflake Streams and Tasks come in — offering a simple yet powerful way to handle Change Data Capture (CDC) and […]

👉 Use staging tables and commit data only after full batch success. For Spark streaming, rely on checkpointing. Idempotent operations like MERGE ensure no duplicates. 👉 Idempotency means re-running the pipeline multiple times gives the same result. Important to avoid duplicates after failure/restart. Implemented using UPSERT/MERGE, unique keys, or partition overwrite. 👉 Track batch metadata […]

When working with Snowflake as a Data Engineer, you’ll often need to move data between your local machine, cloud storage, and Snowflake tables. This is where Stages come into play. Stages in Snowflake are storage locations used for loading/unloading data. They allow you to: Upload local files. Connect to cloud storage (AWS S3, GCS, Azure). […]

Google BigQuery is one of the most popular cloud-based data warehouses, widely used for large-scale analytics. As a Data Engineer, knowing the right commands not only saves time but also ensures efficient resource utilization and cost optimization. Here are the Top 20 BigQuery commands with examples you should add to your toolkit. 1. Show All […]

➤ Tips on caching, clustering keys, partitioning, and query tuning Snowflake is a powerful cloud data platform designed for scalability and simplicity. However, even with its architecture optimized for performance, following best practices can further enhance query speed, reduce costs, and improve reliability. Whether you’re working with large datasets or running complex analytics, performance optimization […]

➤ Types of caches and how to manage them One of Snowflake’s most powerful performance-enhancing features is its caching mechanism. By intelligently storing and reusing data, Snowflake reduces compute overhead, accelerates query execution, and minimizes costs. For data professionals, understanding how caching works and how to manage it effectively can make a significant difference in […]

➤ Best practices for performance and compatibility Loading data efficiently is a crucial part of working with Snowflake. Snowflake supports a variety of file formats including CSV, JSON, Parquet, and ORC, allowing users to ingest structured and semi-structured data with ease. However, choosing the right format and applying best practices ensures better performance, faster queries, […]

➤ Use cases and benefits for collaboration In today’s interconnected data landscape, sharing information across teams, departments, or even external organizations is essential. However, traditional data sharing methods often involve copying large datasets between systems, leading to increased costs, data inconsistencies, and security concerns. Snowflake Data Sharing solves these challenges by allowing you to share […]

➤ How to create instant clones without additional storage costs Managing data efficiently while keeping costs low is a top priority for data teams. Snowflake’s Zero-Copy Cloning is a game-changing feature that allows you to create copies of databases, schemas, or tables instantly — without duplicating data or incurring extra storage costs. In this blog, […]