ShaikBlog

  Current Sybase Environment Q: Which version of Sybase ASE are we using? Are there multiple instances/environments? A: We’re currently on Sybase ASE 15.7, with separate dev, test, and production instances. Dev and test run on version 15.0 but production is on 15.7. Q: Are there any custom configurations (e.g., page size, tempdb, buffer cache tuning)? A: Yes, the page size is set to 8 KB instead of the default 2 KB for better I/O efficiency. We also tuned the buffer cache to 4 GB due to high concurrent access. Tempdb is isolated on a separate disk for performance. Q: What is the size of each Sybase database, and how fast is it growing? A: The main OLTP database is around 500 GB, growing at approximately 10 GB per month due to heavy transaction volume. The reporting database is 1.2 TB but grows more slowly. Q: Are we using any Sybase-specific features (like partitioning, text/image datatypes, LOBs, proxy tables)? A: Yes, we use partitioned tables on the sales data for pe...

 A list of targeted technical questions you can ask about the Sybase databases, objects, tooling, and challenges . These questions will help you: Understand the current Sybase environment deeply Spot complexities that could affect migration Identify areas where your Sybase expertise will be most valuable 🔍 Current Sybase Environment Which version of Sybase ASE are we using? Are there multiple instances/environments? Are there any custom configurations (e.g., page size, tempdb, buffer cache tuning)? What is the size of each Sybase database, and how fast is it growing? Are we using any Sybase-specific features (like partitioning, text/image datatypes, LOBs, proxy tables)? How is user authentication handled in Sybase (e.g., LDAP integration, roles, login triggers)? 🧱 Database Schema & Code What’s the complexity of the schema—do we have deeply nested views, or circular dependencies? How many stored procedures, triggers, and user-defined func...

  Sybase ASC vs. Sybase Database – What’s the Difference? 1. Sybase Database (General Term) "Sybase database" is a generic term that refers to any database system developed by Sybase, Inc. Over the years, Sybase has created several database products, including: Sybase ASE (Adaptive Server Enterprise) – the most well-known, high-performance relational database. Sybase ASA (Adaptive Server Anywhere) – designed for mobile and embedded systems (later became SQL Anywhere ). Sybase IQ – a column-based database for analytics and reporting. Sybase Replication Server , etc. So, when someone says "Sybase database" , they could be referring to any of these products. 2. Sybase ASC (Adaptive Server Cluster Edition) Sybase ASC is a specific edition of Sybase ASE (Adaptive Server Enterprise) designed to support database clustering . It allows multiple ASE instances to work together as a single clustered database system . Designed for high avail...

SQL interviews can be tricky! Here are some thought-provoking questions that tested my knowledge: 1️⃣ How do you calculate the median salary in a department using SQL? 2️⃣ What’s the difference between correlated and non-correlated subqueries? 3️⃣ How can you update data in one table based on values from another? 4️⃣ What are window functions and how are they different from GROUP BY? 5️⃣ How do you find gaps in a sequence (like missing invoice numbers)? 6️⃣ How would you delete every nth row from a table? 7️⃣ How can you transpose columns into rows (unpivot) in SQL? 8️⃣ What’s the purpose of the COALESCE function, and how is it different from ISNULL or NVL? 9️⃣ How do you calculate the difference in days, months, or years between two dates? 🔟 How do you write a SQL query to show cumulative percentage of sales per region? 1️⃣1️⃣ How do you rank items within partitions based on a custom sorting logic? 1️⃣2️⃣ What’s the difference between EXISTS and IN? 1️⃣3️⃣ How can you det...

  Spark Internals & Execution 1. Explain the full lifecycle of a Spark job — from code to DAG to task execution. Definition: The Spark job lifecycle begins when the driver program initiates an action (e.g., .collect() , .count() ). Here's a step-by-step breakdown: User Code: You write transformations and actions in PySpark. Logical Plan: Spark creates a logical plan based on your transformations. Optimization: Catalyst optimizer optimizes the logical plan. Physical Plan: Spark creates a physical plan with stages. DAG (Directed Acyclic Graph): Stages are translated into a DAG of tasks. Task Scheduling: Tasks are distributed across executors. Execution: Executors run the tasks in parallel. Healthcare Example: In processing electronic health records (EHR), you may filter patients by diagnosis, join with lab results, and then aggregate. Spark translates this pipeline into optimized stages and tasks. 2. What is the difference between transformations and actions? Why are tra...

  Real-world examples, optimal solutions, and expert insights for Azure Data Engineer candidates. 🔁 1. Dynamic Pipelines Q: How would you design a single pipeline to process multiple country files from different folders in ADLS? Answer: Use parameterization and ForEach activity to loop through country folders dynamically. Leverage Get Metadata and Lookup to fetch file names and directory structure, then pass those values into a single generic Copy Activity. Example: Imagine folders like /data/US/ , /data/IN/ , /data/UK/ —all with daily CSVs. Create a parameter for CountryName . Use a Lookup to fetch the list of countries (e.g., from a control table or config file). Use ForEach to iterate and process files using a dataset parameterized by @pipeline().parameters.CountryName .  2. Schema Drift Handling Q: What if incoming files have columns frequently added or removed? Answer: Enable Schema Drift in ADF’s mapping data flows. Use the Auto-Mapping feat...