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Architecture and Concepts -

1. What are Databricks? Explain Databricks vs Apache Spark.

Databricks is a cloud computing platform powered by Apache Spark. Databricks brings many improvements to Apache Spark in terms of infrastructure, collaboration, security, and performance. For people new to Spark & Databricks and want to know the differences between these two platforms in detail, a good choice would be taking a Databricks Course in Delhi.

2. Explain the Databricks Lakehouse architecture.

It is an architecture that blends flexibility of Data Lake with reliability of a Data Warehouse. Through Delta Lake built on cloud storage, it enables analytics, streaming, ML, and reporting in one place.

3. List the three major planes in Databricks architecture?

Control plane: Handles notebooks, jobs, workspace management, and user interactions.

Compute plane: Enables running of spark clusters and processing.

Storage plane: Files stored in cloud storage such as S3, ADLS, and GCS.

4. What is Databricks Runtime (DBR)?

Databricks Runtime is the software running on Databricks clusters; Apache Spark, Delta Lake, Photon Engine, enhanced connectors and performance enhancements for faster and more reliable processing.

5. Difference between All-Purpose Cluster and Job Cluster?

All-purpose clusters are for development, testing, and interactive use. Jobs that are scheduled automatically create job clusters and terminate when completed, which helps to keep overall costs down.

6. What is a Databricks Unit (DBU)?

DBU—Databricks Unit of Billing They measure processing power consumption of clusters by type of machine, workload and compute resources consumed during execution.

7. What is the DBFS (Databricks File System)?

DBFS is a distributed file system that offers a simple interface to cloud storage. It lets users get at files by means of simple commands, without having to know complex storage paths.

8. What languages may I use to construct a Databricks notebook?

Databricks notebooks support Python, SQL, Scala and R. You can switch between these languages for various jobs in the same notebook using magic instructions.

9. How do you store secrets (like API keys) in Databricks?

Secret scopes let you store passwords, tokens and API keys in Databricks securely. They are provided to apps at run-time, so sensitive information is not hardcoded.

10. What is Unity Catalog?

Databricks’ centralized governance product is called Unity Catalog. It offers granular security controls for access, data lineage, auditing and data discovery across workspaces.

Storage & Delta Lake

11. What is Delta Lake?

Delta Lake is a layer on top of parquet files that adds ACID transactions, transaction logs and reliable data management to batch and streaming operations.

12. What are Delta Lake ACID transactions?

Atomicity: Entire transaction is successful or unsuccessful.

Consistency: The database is in a consistent state.

Isolation: Transactions can operate independently without affecting other transactions.

Durability: Once committed, data persists regardless of any failure.

13. What is Delta Lake ‘time travel’?

Time Travel allows users to access older versions of a Delta table. It is useful for auditing, troubleshooting, retrieving and replicating old reports or machine learning results.

14. What is the use of OPTIMIZE command?

The OPTIMIZE command combines many little files into a few large files. It reduces the overhead of metadata, and the data read is faster. This means greater query performance.

15. What do you mean by Z ordering?

Z-Ordering groups data by most-filtered columns. This enables Spark to skip more data, read fewer files and speeds it up.

16. What is the use of the VACUUM command?

VACUUM cleans up old files that are no longer used by the current version of the table. It saves space, but limits access to old copies through Time Travel.

17. Explain Schema Enforcement and Schema Evolution.

Schema enforcement failure when data not according to table schema.

Schema Evolution: The schema can be automatically extended with new columns when new data arrives.

18. What is a Delta Table Manifest File?

The Delta table has a Manifest File that lists the active Parquet files. This means that external programs like Athena or Presto can read Delta data without any knowledge of Delta logs.

19. How to do Slowly Changing Dimension(SCD) Type 2 in Delta Lake?

SCD Type 2 - It maintains history by adding new rows. The MERGE command updates old records and inserts fresh current records.

20. What’s Changed Data Feed ( CDF )?

Change Data Feed records insert, update and delete activity on entries in Delta tables. This allows the construction of efficient incremental ETL pipelines without having to process full datasets.

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ETL Pipeline & Data Engineering

21. Medallion Architecture: What’s that?

The Medallion Architecture contains three levels of data: Bronze being the raw data, Silver being the clean and validated data, and Gold being the business-ready reporting and analytics data.

22. What is an autoloader in Databricks?

Auto Loader automatically finds and processes new files as they land in cloud storage. It can efficiently consume a big scale of data. It isn't necessary to scan the storage sites over and over again.

23. How does schema inference operate in Auto Loader?

Auto Loader automatically detects the data structure from incoming files. It offers schema evolution, schema hints and rescued data columns to gather unexpected or mismatched data.

24. What is Delta Live Tables (DLT)?

Delta Live Tables is a managed ETL framework that makes building pipelines easier. You write the transformations in SQL or Python and Databricks takes care of orchestration, monitoring and reliability. Learning Delta Live Tables, pipeline automation, and advanced ETL development skills by opting for Databricks Training in Gurgaon will assist professionals in building production-grade data pipelines effectively.

25. What can you expect from Delta Live Tables?

DLT pipelines have expectations that act as their data quality guidelines. If the quality standards are not met, they could upload wrong records, dump bad data or stop the pipeline.

26. Difference between Databricks Batch and Streaming.

Batch processing: High delay because of specified data batches.

Streaming Processing: Apply Structured Streaming and other Data Frame APIs for real-time processing of continuous data.

27. What is Checkpointing in Spark Streaming?

A checkpoint is a durable representation of the progress and state of the stream. Spark will restart from the last checkpoint it wrote if anything goes wrong.

28. How to perform a “Merge” (Upsert) in Spark SQL?

I want to upset (update existing records, insert new records) into a delta table matching on particular criteria in 1 atomic transaction. You can do this using the MERGE command.

29. What is the "Copy Into" command?

COPY INTO copies files from cloud storage into delta tables. It automatically keeps track of files already processed and does not load them twice which is helpful for repeated ingest operations.

30. What is a "UDF" and would you use one?

UDFs let you apply custom logic using python or scala programming. Don't overdo it. Built-in procedures in spark are faster and more efficient.

Performance Optimization & Tuning Services

31. Explain the shuffle in spark and why does it cost more?

Data shuffling involves transferring data from one executor to another. It includes disk I/O and data transmission, which are costly processes.

32. How can you minimize shuffles in your code?

Early pre-join data filtering

Use Broadcast Joins for tiny tables.

Z Order for Data Layout Optimization

Run AQE for better execution time. 2.

33. What is broadcast join?

Join transmits tiny tables into memory on all the executors. This avoids transferring the bigger table over the network, and speeds the joins.

34. What is AQE (Adaptive Query Execution)?

AQE: Improves performance of the query throughout execution time. It can automatically combine small partitions, change join algorithms and handle skewed data using real run-time stats.

35. How do you handle the “Data Skew”?

Data Skew happens when certain keys contain much more data than others. Possible solutions to this include key salting, partitioning or AQE skew optimization.

36.What is Caching in databricks?

It involves storing Data frames in memory/ disk so that they can be reused. It makes processes very fast since it prevents unnecessary computation of the data by spark.

37. What is Spark UI? What purpose is it?

Spark UI helps you monitor and debug your Spark jobs. It gives information about job executions, stages, tasks, execution plans, shuffle activity, memory utilizations and performance issues.

38. What are Spills in Spark?

If the executor memory is insufficient, then a spill happens and data is leaked to disk. Memory accesses are faster, so the performance falls dramatically.

39. How to choose the right amount of partitions?

Normal to have partitions from 128MB to 200MB in size. It depends on the cluster size, number of available cores and amount of data and complexity of the project.

40. What are Photons?

Photon is a high-performance C++ query execution engine. It combines vectorized processing and modern cpu optimizations to speed up SQL queries and DataFrame processing.

Operations, Scenarios & Security

41. How to schedule a notebook in databricks?

We are using Databricks Workflows to orchestrate notebooks. Define a Job Assign task Define triggers, such scheduling or file arrival Run it automatically

42. How do you conduct CI/CD for Databricks?

Generally, a Databricks CI/CD consists of: Git integration via Repos Asset Bundle Deployment Terraform based infrastructure management All of the above are environment agnostic.

43. Repos (Databricks Repos) : What is this?

Databricks Repos brings together notebooks and code and git repositories. Version control, branching, code reviews, cooperation, automated deployment processes.

44. How do you handle a failure in the production pipeline?

Look at job logs and error messages.

Check health and resources of the cluster

Verify the quality of the source data.

If needed, do a Time Travel data restore.

Fix the errors and repeat the process.

45. Problem: Slow join between 2 TB and 10 MB tables. How will you solve it?

Broadcast join will be used for the 10 MB table. It eliminates any need to shuffle the larger 2 TB table between executors.

46. How do you restrict access to a table in databricks?

Access to tables is managed by Unity Catalog permissions. Administrators can enable or disable access and establish row-level or column-level security controls.

47. What is the function of the “Ganglia” User Interface?

Ganglia let you monitor your cluster infrastructure such CPU usage, memory utilization, network activities, etc. It’s a question of resources.

48. How do you handle PII data?

Control access with Unity Catalog.

Masking at the table column level

Encrypt data at rest and in transit.

Critical data stored in the safe area.

49. What is Dynamic File Pruning & Data Skipping?

Data Skipping uses statistics on the files to skip files not containing needed data.

Dynamic File Pruning: Join filters to prune superfluous file reads at run time.

50. What is wrong with the “Small File Problem”? How does Databricks address the problem?

More metadata overhead Slow queries Too many tiny files Databricks uses OPTIMIZE, Auto-Compaction and Optimized Writes to create larger files.

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Sum up,

Databricks has emerged as one of the most prominent platforms for data engineering today. Knowing about some basic concepts related to Databricks, Delta Lake capabilities, performance optimization techniques, securing your work, and pipeline management will help you do well in interviews and actual projects. To acquire knowledge about all these concepts, you can register for a Databricks course in Noida. These questions address all those topics that are commonly discussed by employers and form a solid base for individuals aspiring to build their careers in data engineering using Databricks.