Databricks vs Snowflake

 Databricks vs Snowflake — Choosing the Right Engine for Your Data Strategy

When building scalable data platforms, two giants often come into play: Databricks and Snowflake. While both run seamlessly on top cloud providers like AWS, Azure, and GCP, they are optimized for different workloads and use cases.
🧱 Databricks is built around Apache Spark and excels in:
1. Unified data analytics and machine learning workflows
2. Delta Lake support for lakehouse architecture
3. Real-time streaming and batch processing
4. Advanced scheduling and workflow orchestration
5. Deep learning, AI model training, and MLOps pipelines
6. Interactive visualizations and reporting

❄️ Snowflake, on the other hand, is designed for:
1. Multi-cluster scaling with independent compute and storage
2. Seamless handling of structured and semi-structured data
3. High performance with minimal tuning via automation
4. Easy integration across diverse source systems
5. Security-first data governance and compliance
6. In-platform BI capabilities for business users

Bottom line:
-> Use Databricks for heavy data engineering, AI/ML, and advanced real-time processing.
-> Choose Snowflake for high-speed querying, reporting, and simplified analytics workloads.

As a Senior Data Engineer, I’ve found hybrid architectures leveraging both platforms offer the best of both worlds scalable compute with Databricks and agile warehousing with Snowflake.

Data Engineering Tools

 

In Data Engineering, tools are everywhere — but value comes from how and why you use them, not how many logos you know.
Here’s how to think about the modern data engineering stack from a practitioner’s lens 👇
1️⃣ Ingestion – Airbyte, Fivetran, Kafka
Reliable movement > just pulling data (handle schema drift, latency, failures)
2️⃣ Storage – S3, Snowflake, BigQuery, Delta Lake
Design for scale, cost, and downstream usage
3️⃣ Processing – Spark, Flink, Trino, Databricks
Pick the right engine for the workload — not Spark for everything
4️⃣ Orchestration – Airflow, Prefect, Dagster
Pipelines should be observable, retry-safe, and predictable
5️⃣ Transformation – dbt & ELT tools
Clean logic = trustworthy analytics
6️⃣ Quality & Governance – Great Expectations, Atlas
Data quality isn’t optional — it’s engineering
7️⃣ Monitoring & DevOps – Docker, K8s, Prometheus
Deliver data as a product, not a fragile pipeline
8️⃣ Visualization – Power BI, Tableau, Looker
Data matters only when it drives decisions
🔑 Takeaway:
Strong data engineers don’t chase tools.
They design scalable, reliable systems — and choose tools that fit the need.

How LLM works End-to-END

 

𝐌𝐨𝐬𝐭 𝐩𝐞𝐨𝐩𝐥𝐞 𝐮𝐬𝐞 𝐋𝐋𝐌𝐬 𝐝𝐚𝐢𝐥𝐲 𝐛𝐮𝐭 𝐡𝐚𝐯𝐞 𝐧𝐨 𝐢𝐝𝐞𝐚 𝐰𝐡𝐚𝐭 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐡𝐚𝐩𝐩𝐞𝐧𝐬 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐭𝐡𝐞𝐢𝐫 𝐩𝐫𝐨𝐦𝐩𝐭 𝐚𝐧𝐝 𝐭𝐡𝐞 𝐫𝐞𝐬𝐩𝐨𝐧𝐬𝐞.

Here are the optimized LLM workflows for 30+ production systems. The teams that understand these internals make fundamentally different and better architectural decisions.

Here is what actually happens when you hit "send" on a prompt:

Phase 1: Input Processing (Green Layer)

1. Input Text
• Raw text: "The dog ran up the hill"
• This is what users see natural language

2. Token Embeddings
• Text → subword tokens, mapped to IDs
• "The", "dog", "ran", "up", "the", "hill" become discrete units
• Why it matters: Token boundaries affect cost and context limits

3. Positional Embeddings
• Each token gets position information (binary vectors)
• The model learns "dog" in position 2 relates to "ran" in position 3
• Why it matters: Without positional encoding, word order is meaningless

4. Final Input Embedding
• Token embeddings + positional embeddings = rich vector representation
• This combined representation enters the transformer
• Why it matters: Quality here determines quality downstream

Phase 2: Transformer Block (Red Layer): N×N Layers

1. Multi-Head Self-Attention
• Model computes relationships between all tokens simultaneously
• "cat sat on mat" → each word attends to every other word
• Why it matters: This is where context understanding happens

2. Residual Connection & Layer Normalization (×2)
• Prevents gradient vanishing in deep networks
• Maintains information flow through many layers
• Why it matters: Enables scaling to 100+ billion parameters

3. Feed-Forward Network
• Dense neural network processes attended representations
• Non-linear transformations extract patterns
• Why it matters: This is where the actual "reasoning" computation happens

Repeated N times (GPT-4 has ~120 layers)
• Each layer refines understanding progressively
• Early layers: syntax and structure
• Middle layers: semantics and relationships  
• Later layers: task-specific reasoning

Phase 3: Prediction and Generation (Blue Layer)

1. Logits → Softmax
• Model outputs probability distribution over vocabulary
• "hill: 0.72, road: 0.08, yard: 0.06, path: 0.02"
• Why it matters: These probabilities determine output quality

2. Sampling Strategy
• Greedy: Always pick highest probability (deterministic, boring)
• Temperature: Control randomness (higher = more creative)
• Top-P: Sample from top probability mass (balanced approach)
• Why it matters: Same logits + different sampling = completely different outputs

3. Output Token
• Selected token: "hill"
• Process repeats for next token until completion
• Why it matters: Generation is iterative each token depends on previous tokens

ETL vs. ELT vs. ETLT: What’s the Real Difference?

Here is the distinct function of each approach based on modern architecture needs 👇

📌 1. ETL (Extract, Transform, Load) — "The Classic"
Process: Data is extracted ➡ Transformed in a separate staging server ➡ Loaded into the Warehouse.
Best For: Complex transformations, strict security/compliance masking before data lands, or legacy on-prem systems with limited compute.

☁️ 2. ELT (Extract, Load, Transform) — "The Modern Standard"
Process: Extract raw data ➡ Load immediately into the Warehouse ➡ Transform using SQL/dbt inside the warehouse.
Best For: Modern Cloud Data Warehouses (Snowflake, BigQuery, Redshift) where storage is cheap and compute is massive.

⚖️ 3. ETLT (Extract, Transform, Load, Transform) — "The Hybrid"
Process: Lightweight cleaning (PII masking) before loading ➡ Heavy analytics transformations after loading.
Best For: When you need both strict Data Quality checks (pre-load) and complex analytical modeling (post-load).