About this blog

I'm Dinesh Chandra RS — a Senior Data & AI Engineer based in India with 4+ years of hands-on experience building production-grade data platforms. I started DataLane to share the patterns, trade-offs, and hard lessons that don't make it into vendor docs: real architectures on Snowflake, Databricks, AWS, and Azure, written clearly enough to use the same day you read them.

The mission

Make the data stack practical for every data role — real patterns that work in production, written clearly enough to use the same day.

Rellapati Srinivasa Dinesh Chandra

Senior Data & AI EngineerSnowPro CoreSnowPro Specialty: Gen AI

Work with me / advertise

Data & AI Engineer with 4+ years of experience designing and delivering end-to-end, cloud-native data platforms on Snowflake, Databricks, AWS, and Azure. I specialize in Medallion (Bronze–Silver–Gold) architectures built with dbt, SnapLogic, PySpark, and Apache Airflow — plus AI agents and Snowflake Cortex solutions, REST APIs with FastAPI, and Datadog-based monitoring. Every post on this blog comes from that production work.

Years in Data Engineer

136

Articles published

Cheat sheets

Stacks covered

Featured work

Medallion platforms on Snowflake— Bronze–Silver–Gold with dbt, Streams & Tasks, and SnapLogic ingest, exposed through FastAPI and Streamlit-in-Snowflake.
Cortex AI in production— LLM-backed agents and natural-language analytics next to governed Gold tables.
Multi-cloud pipelines— AWS (S3, Glue, Lambda) and Azure (Databricks, Synapse, Data Factory) with Airflow orchestration.

Experience

Data & AI Engineer · Anblicks
Nov 2025 – Present
Hyderabad, India
- Architected an end-to-end Medallion (Bronze–Silver–Gold) data platform on Snowflake, ingesting from heterogeneous sources with SnapLogic.
- Built modular, version-controlled, tested dbt transformations and dimensional models across the Silver and Gold layers.
- Engineered orchestration with Apache Airflow and Snowflake Streams & Tasks for incremental and near real-time processing.
- Developed AI agents and Snowflake Cortex AI functions delivering LLM-powered data products and natural-language analytics.
- Exposed governed Gold-layer datasets through a FastAPI service layer and built Streamlit-in-Snowflake apps for interactive validation.
- Implemented data validation across all layers with Datadog monitoring for pipeline health, SLAs, and freshness.
Data Engineer · LTIMindtree
Dec 2024 – Nov 2025
Chennai, India
- Designed scalable ETL/ELT pipelines integrating Microsoft Dynamics 365 CRM and other sources into the enterprise data platform.
- Built layered transformation workflows with dbt, PySpark, and Databricks using Medallion-style modeling.
- Orchestrated pipelines with Apache Airflow, optimizing workflows to cut processing time.
- Built error-handling and reconciliation mechanisms ensuring end-to-end data accuracy.
Data Engineer · Psiog Digital
Jul 2022 – Aug 2024
Chennai, India
- Implemented data pipelines and ETL with Python, PySpark, and SQL across AWS (S3, Lambda, Glue, SNS) and Azure (Databricks, Synapse, Data Factory).
- Developed and maintained Snowflake-based warehousing and transformation solutions.
- Automated data validation and quality checks; containerized workflows with Docker.
- Delivered analytics and reporting with Incorta and OneStream.

Technical skills

Cloud & Warehouses

Snowflake (Streams, Tasks, Cortex AI)
Databricks
AWS (S3, Lambda, Glue, SNS)
Azure (Synapse, Data Factory)

Data Engineering & ETL/ELT

dbt
SnapLogic
Apache Airflow
Apache NiFi
PySpark
Medallion Architecture
Data Modeling

AI & Machine Learning

AI Agent Development
Snowflake Cortex
NLP
TensorFlow
scikit-learn
pandas & NumPy

Programming

Python
SQL
PySpark
C#

APIs & Apps

FastAPI
Streamlit (in Snowflake)
ReactJS

DevOps & Quality

Docker
GitLab CI/CD
Azure DevOps
Datadog
Data Validation & Testing

Certifications & education

SnowPro Specialty: Gen AI Certification

Snowflake · recently earned

SnowPro Core Certification

Snowflake

Incorta Developer

Incorta (Dec 2023)

Incorta Essentials

Incorta (Nov 2023)

B.Tech, Mechatronics Engineering

Sastra University · 2018 – 2022

What this blog covers

45 production stacks.Full catalog →

Orchestration

Airflow

Workflow orchestration: DAGs, scheduling, and operations.

Dagster

Software-defined assets, asset checks, and data-aware orchestration.

Prefect

Pythonic flows, deployments, and lighter-weight scheduling.

Apache NiFi

Visual flow-based ingestion, routing, and edge collection.

Compute & processing

Apache Spark

Distributed processing with PySpark and Spark SQL.

Apache Flink

Stateful stream processing, watermarks, and exactly-once sinks.

Apache Beam

Portable batch and streaming pipelines — Dataflow, Flink, Spark runners.

Hadoop

HDFS, YARN, Hive, and the on-prem stack still running in many banks.

Streaming & CDC

Kafka

Event streaming, topics, Kafka Connect, and real-time pipelines.

Apache Pulsar

Multi-tenant messaging with segment storage and built-in functions.

Debezium

Change data capture from Postgres, MySQL, and SQL Server into Kafka.

Transformation

dbt

SQL transformation, testing, and analytics engineering.

SQLMesh

Virtual environments, column-level lineage, and plan-based SQL transforms.

Warehouses & analytics

Snowflake

Cloud warehousing, performance, and cost control.

Databricks

Lakehouse architecture, Delta Lake, Unity Catalog, and notebooks.

BigQuery

Serverless analytics on Google Cloud.

Amazon Redshift

RA3, Spectrum, concurrency scaling, and the AWS warehouse.

ClickHouse

Columnar OLAP for high-cardinality analytics and real-time inserts.

DuckDB

In-process analytics and lightweight local pipelines.

Microsoft Fabric

OneLake, Lakehouse, Data Factory, and Power BI in one Microsoft plane.

Trino

Federated SQL across Iceberg, Hive, and warehouses without moving data.

Lakehouse & table formats

Iceberg

Open table format, catalogs, and multi-engine lakehouses.

Apache Hudi

Incremental upserts, copy-on-write vs merge-on-read, and streaming lakes.

Ingestion & ELT

Airbyte

Open-source ELT connectors into warehouses and lakes.

Fivetran

Managed ELT with schema drift handling and usage-based pricing.

Quality & modeling

Data Modeling

Star schemas, SCDs, Data Vault, and warehouse design.

Data Quality

Contracts, Great Expectations, Soda, dbt tests, and freshness SLAs.

Cloud platforms

AWS

Glue, Kinesis, Redshift, S3, Athena, EMR, and the AWS data platform.

Azure

Data Factory, Synapse, Event Hubs, and Microsoft Fabric.

GCP

BigQuery, Dataflow, Pub/Sub, Dataproc, and Composer.

Platform & IaC

Terraform

Infrastructure as code for warehouses, IAM, and data accounts.

Kubernetes

Airflow, Spark, Flink, and Kafka operators in production clusters.

Docker

Local stacks, image discipline, and the unit of pipeline compute.

Languages & local engines

Python

pandas, data quality, and pipeline engineering in Python.

SQL

Query patterns, window functions, and modeling.

Polars

Lazy, multi-threaded DataFrames that replace pandas on large local jobs.

Scala

The Spark and Flink JVM language still required in many platforms.

Operational data stores

PostgreSQL

The OLTP source of truth: CDC, logical replication, and indexing.

MongoDB

Document sources, change streams, and how they land in the warehouse.

Cassandra

Wide-column serving stores and the modeling rules that keep them fast.

Redis

Caches, feature serving, and when not to treat it as a database.

Elasticsearch

Search, logs, and OpenSearch as a serving plane beside the warehouse.

Serving & BI

BI & semantic layer

Looker, Tableau, Power BI, Superset, and the metrics layer.

AI & MLOps

AI & GenAI

LLMs, RAG pipelines, vector search, and AI on your data.

MLOps

Feature stores, model pipelines, and ML infrastructure.

How we work

Hands-on first. Every tutorial is runnable — real code, real commands, no pseudocode.
Honest reviews. Weaknesses get named. A tool that isn't worth your money gets said out loud.
Kept current. Cloud pricing and APIs drift; posts get reviewed and updated dates when they change.
Clearly labeled monetization. Sponsored posts carry a badge, affiliate posts carry a disclosure — every time.

Original writing, not scraped docs

DataLane is a publisher site. Articles are written from production work on Snowflake, Databricks, Airflow, and dbt — incident notes, query profiles, and cost surprises — not copied vendor documentation or rewritten RSS. Interview questions describe theshape of a screen; they are not leaked exams. Certification practice items are original and labeled as unofficial. Display ads, when Google approves them, appear only on long original guides, never on coding pads, quizzes, or account pages.

How this site makes money

Some posts contain affiliate links or are sponsored by companies in the data space. Sponsored posts are always labeled, and affiliate relationships are disclosed at the top of each post. Read the fullaffiliate disclosure — or, if you'd like to sponsor a post, see the advertise page.

Want to write a guest tutorial? See Write for us. Otherwise head to the contact page. New here? Start with the blog, theninterview prep orcertification practice.