MR901 · August 18, 2025 05:39
diff --git a/databrick_evaluation_role_wise_suitability.csv b/databrick_evaluation_role_wise_suitability.csv
SNo	Role	Domain	Best Tools/Platform	Why Use Databricks	Limitations in using Databricks	Recommendation on Databricks
1	Data Scientist	Structured Tabular Data	Databricks, BigQuery, Redshift, VMs, Local Env	Seamless data lake integration, Delta Lake, good AutoML pipelines, strong at aggregations and joins	Less useful for low-latency modeling, slow to deploy fine-tuned models to prod	Strongly recommended as a primary tool for structured data prototyping and experimentation.
2	Data Scientist	Time Series (sensor, finance)	Azure Data Explorer, Prophet, Darts, GluonTS, GCP AI Forecasting, Kafka Streams	Can handle large-scale time series processing and feature engineering	Lacks ready-to-use TS-specific models and operational forecasting toolkits	Partially recommended as a side tool for feature generation, not final modeling.
3	Data Scientist	Signal Processing	MATLAB, SciPy, Local Env, Librosa, Wavelet Toolbox	Useful for distributing raw signal data processing at scale	Not natively signal-processing friendly, visualization and debugging are poor	Not recommended. Prefer MATLAB or Python ecosystem locally or on edge.
4	Data Scientist	Computer Vision (CV)	Local GPUs, GCP/AWS VMs, TensorFlow Hub, PyTorch on Notebooks, NVIDIA Triton	Easy to experiment at scale, decent Spark integration for preprocessing large image/video metadata	No native GPU support in notebooks, inefficient image/video batch handling, lack of model deployment readiness for CV	Not ideal. Use only as a side tool for data prep, not for CV modeling or deployment.
5	Data Scientist	NLP (LLM, transcription, etc.)	HuggingFace, LangChain, GCP Vertex AI, OpenAI APIs, Colab Pro, Local + VSCode + Docker	Databricks supports experiment tracking with MLflow and distributed preprocessing of NLP pipelines	Not optimized for transformer models, lacks tokenizer-level debugging, poor GPU-native workflow	Use only for data processing and logging; not suitable for model fine-tuning or serving.
6	ML Engineer	Structured Tabular	Databricks, TFX, Vertex AI Pipelines, Kubeflow, SageMaker	Great for experimentation, feature stores, experiment tracking	Lacks full lifecycle automation unless extended heavily with external infra	Recommended as a side tool in ML workflow. Use Vertex/SageMaker for end-to-end.
7	ML Engineer	Time Series	Kubeflow, MLflow, Darts, Prophet, TensorFlow Extended (TFX), Kafka + Flink	Good for feature engineering at scale, Delta Lake for versioning time-series data	No native support for real-time forecasting pipelines, weak integration with edge deployment	Partially recommended for feature engineering but not for deployment.
8	ML Engineer	Signal Processing	Apache Beam, TensorFlow DSP, NVIDIA RAPIDS, MATLAB Production Server	Can preprocess large signal datasets (e.g., audio, IoT) using Spark	No specialized libraries for DSP (e.g., no built-in FFT optimizations), poor latency for real-time signals	Not recommended. Use edge-compatible frameworks (TensorFlow Lite, NVIDIA Jetson) instead.
9	ML Engineer	Computer Vision	NVIDIA Triton, AWS Inferentia, ONNX Runtime, Docker on GCP/Azure	Databricks can support early experimentation, large metadata handling	Model quantization, ONNX conversion, and inference benchmarking are not possible	Not suitable as a primary or side tool. Use infra-native ML serving platforms.
10	ML Engineer	NLP	HuggingFace, Ray, TFX pipelines, LangChain on GPUs	Databricks can support tokenization and preprocessing at scale	Lacks real-time performance profiling, distributed inference support	Use only for prep, not training or prod deployment.
11	Data Engineer	Structured Tabular	Spark (Databricks or EMR), Delta Lake, Snowflake, Airflow, DBT, BigQuery	Delta Lake integration, Spark pipelines, data lineage with Unity Catalog. Best-in-class for large-scale ETL, ACID compliance, schema evolution	Less transparency in cluster-level optimization, job observability issues, Overkill for simple transformation, and small datasets	Recommended with caution as a primary tool, especially with Spark-native pipelines. Strongly recommended for enterprise data lakes.
12	Data Engineer	Time Series	Delta Lake, Apache Kafka, InfluxDB, TimescaleDB, BigQuery	Efficient partitioning for time-series data, scalable backfilling, ability to handle bulk ingestion	No native time-series optimizations (e.g., downsampling, retention policies), long-term historian role not suitable	Avoid as primary, use as batch processor but not for real-time/historical analytics. Optimal to use with specialized DBs for queries.
13	Data Engineer	Signal Processing	Apache Kafka, Spark Streaming, Parquet/Delta for storage	Handles high-volume sensor/IoT data ingestion and partitioning	No support for signal-specific transformations (e.g., spectrograms)	Partially recommended. Use only for raw data landing, not signal processing.
14	Data Engineer	Computer Vision	Apache Spark, Delta Lake, AWS/GCP storage, OpenCV (batch processing)	Efficiently handle large-scale image/video metadata, Delta Lake for versioning	No GPU-accelerated ETL, limited support for binary data (images/videos) in pipelines	Not suitable. Use only for metadata management, not raw image processing.
15	Data Engineer	NLP	Spark NLP, HuggingFace Datasets, Delta Lake, Airflow for pipeline orchestration	Distributed text preprocessing, schema enforcement for NLP datasets	No native tokenizer support, inefficient for large-scale embeddings storage	Recommended as a side tool. Use only for text data cleaning and storage, not for modeling.
16	MLOps Engineer	All Domains	MLflow (standalone), TFX, SageMaker, Kubeflow, Vertex AI, Weights & Biases	Databricks provides a unified MLflow experience and native tracking	Deployment, CI/CD, rollout strategies, drift detection are weak	Not recommended as a primary tool. Use for logging; plug into stronger MLOps stack.
17	Software Developer	All Domains	PostgreSQL, Superset, Dash, Streamlit, Flask, REST APIs	Databricks adds little value.	No native APIs, security and modularity limitations, no CI/CD integration, reproducibility weak across dev and prod, not software-dev focused	Avoid as a primary or side tool. Not aligned with software dev lifecycle. Use backend deployment infra.
18	Data Analyst	Structured Data	PowerBI, Tableau, Google Sheets, Excel, Looker	SQL interface and notebook UI is usable	Learning curve steep, interface not as intuitive for analysts	Avoid as primary tool, possible side tool for custom analysis via notebooks.
19	Data Architect	Structured and Unstructured	GCP/AWS Infra, Snowflake, Delta Lake, Airflow, S3, Redshift	Good for integrating data lakes, building unified lakehouse, Unity Catalog for governance	Abstraction over infra may hinder low-level optimization, poor metadata modeling	Recommended only for lakehouse designs; avoid where tight DB control is needed.
20	Edge/Embedded AI Engineer	Signal Processing, Computer Vision, Time Series	NVIDIA Jetson, Edge TPU, Coral, Local Dev + Docker + TF Lite	Almost no support for edge-optimized pipelines, quantization, or model export	No edge-targeted runtime or memory profiling tools	Not recommended. Use edge-specific toolkits and inference frameworks.