Brolly Academy | Snowflake
Snowflake vs Databricks
Architecture, Pricing, AI and ML, Open Formats, When to Choose Each and Which to Learn First (2026)
A plain-English comparison of Snowflake and Databricks for October 2026: how each one is built, a feature-by-feature table, how credits and DBUs work, where Iceberg and Delta fit, AI features, how companies use both together, what Indian job data shows, and which platform you should learn first.
Snowflake vs Databricks compared: architecture, credits vs DBUs pricing, AI and ML, open formats, when to choose each, careers in India and what to learn.
By Brolly Academy Team · Brolly Academy · Updated 24 October 2026
Table of Contents
1. Snowflake vs Databricks at a glance
2. What each platform is, in one minute
3. Architecture compared: warehouse vs lakehouse
4. Side-by-side comparison, feature by feature
6. AI and machine learning compared
7. When to choose Snowflake and when to choose Databricks
8. How Snowflake and Databricks work together
11. Which should you learn first? A guide by background
Quick answer
Snowflake is a fully managed, SQL-first cloud data platform that grew out of the data warehouse, while Databricks is a lakehouse platform built around Apache Spark and notebooks that grew out of the data lake; neither is "better" for everyone. Pick Snowflake when most of your work is SQL analytics, reporting and governed data sharing with little infrastructure to manage. Pick Databricks when most of your work is large-scale Spark engineering, data science and machine learning in Python notebooks. In 2026 the two overlap more every year: both support Apache Iceberg, both have SQL compute, and both ship AI agents.
- Snowflake: storage, compute (virtual warehouses) and cloud services as separate layers; priced in credits; strongest for SQL users
- Databricks: lakehouse over your own cloud storage; Delta Lake by default; priced in DBUs plus your cloud bill; strongest for Spark and ML
- Together: many companies land and engineer data in Databricks and serve it in Snowflake, or share Iceberg tables between both
- Careers in India: "snowflake" returned about 25,900 Naukri results on 25 October 2026; SQL appeared in every Snowflake job description we read
- Learn first: SQL, ETL and DBA backgrounds usually start with Snowflake; Python and Spark backgrounds often start with Databricks
Key takeaways
- The core difference is design: Snowflake started as a managed warehouse and added lake features; Databricks started as a Spark lake engine and added warehouse features.
- Snowflake bills compute in credits per second of warehouse time; Databricks bills compute in DBUs, and your cloud provider bills storage and networking separately.
- Open table formats are where they meet: Snowflake supports Apache Iceberg tables, and Databricks uses Delta Lake by default and also supports Iceberg.
- No independent benchmark was verified for this guide, so treat any "X is faster or cheaper" claim online with care. Cost depends on workload, region and how well you tune it.
- For an Indian fresher or SQL developer, Snowflake has the gentler learning curve, but strong SQL plus Python opens doors on both platforms.
1. Snowflake vs Databricks at a glance
Short answer: Snowflake is warehouse-first and SQL-first; Databricks is lake-first and notebook-first; both now cover warehousing, engineering and AI, but each is strongest where it started.
Use this table as your one-screen summary. Every row comes from the vendors' own documentation and pricing pages, read in October 2026.
2. What each platform is, in one minute
Short answer: Snowflake is a managed cloud data platform you mostly use through SQL; Databricks is a unified analytics platform that runs Apache Spark and notebooks on top of your own cloud storage.
You do not need a full Snowflake explainer here. If you are new, read our guide on what Snowflake does first, then come back.
Snowflake runs on Amazon Web Services, Microsoft Azure and Google Cloud. In India, Snowflake lists two regions: AWS Asia Pacific (Mumbai) and Azure Central India (Pune) (Snowflake documentation, October 2026). You load data into tables, run SQL on "virtual warehouses" (independent compute clusters that start, stop and resize on demand) and pay for the time they run. Snowflake handles storage layout, tuning metadata, security and upgrades for you.
Databricks now calls its product the "Databricks Data + AI Platform" (Databricks product page, October 2026). Its documentation describes it as a unified, open analytics platform that integrates with cloud storage and security in your own cloud account and manages the infrastructure for you (Databricks documentation, October 2026). Databricks publishes its documentation for AWS, Azure and Google Cloud. The engine underneath is Apache Spark, an open-source engine that splits big data jobs across many machines.
Two words you will see a lot:
- Data warehouse: a system that stores clean, structured data in tables for fast SQL reporting
- Lakehouse: Databricks' term for one platform that combines the cheap, flexible file storage of a data lake with the reliability and SQL performance of a warehouse
3. Architecture compared: warehouse vs lakehouse
Short answer: Snowflake keeps data in its own managed storage layer (or in Iceberg tables) and runs SQL on separate virtual warehouses; Databricks keeps data as Delta Lake files in your cloud storage and runs Spark clusters or SQL warehouses over it.
How Snowflake is built
Snowflake has three layers (Snowflake documentation, October 2026):
- Database storage: data is stored in Snowflake's compressed columnar format, split into micro-partitions (automatic storage blocks of about 50 MB to 500 MB of uncompressed data). Snowflake tables, Apache Iceberg tables and hybrid tables live here.
- Compute: virtual warehouses run your queries. Each warehouse is independent, so a heavy ETL job on one does not slow a dashboard on another.
- Cloud services: security, authentication, metadata and query optimisation.
Snowflake calls this a hybrid of shared-disk (one shared storage pool) and shared-nothing (MPP compute clusters) designs. For a visual, see our Snowflake architecture diagram post.
How Databricks is built
- Your cloud storage: data files sit in your own cloud account's object storage.
- Delta Lake: Delta Lake is "the default format for all operations on Databricks" (Databricks Delta Lake documentation, October 2026). It is an open-source table format that adds ACID transactions (reliable inserts, updates and deletes) and time travel to Parquet files.
- Compute: scalable compute clusters run Spark jobs and notebooks; SQL warehouses are the compute for SQL users who query through the SQL editor or notebooks.
- Unity Catalog: a unified governance layer for permissions, lineage and auditing across data and AI assets.
What this means for you
Neither design is wrong. Snowflake trades some control for simplicity. Databricks gives you more control, and asks for more engineering skill in return.
4. Side-by-side comparison, feature by feature
Short answer: The biggest practical differences are the storage format, the pricing unit, how you write code (SQL worksheets vs notebooks) and where each is strongest in AI and ML.
Now let us go row by row.
Storage and open formats (Iceberg and Delta)
Apache Iceberg is an open table format: a way to store tables as Parquet files plus metadata so that many engines (Snowflake, Spark, Trino, Flink) can read and write the same data. In Snowflake, an Iceberg table can use Snowflake as its catalog (full read and write, with Snowflake doing maintenance such as compaction) or an external catalog such as AWS Glue. The data can sit in your own S3, Google Cloud Storage or Azure storage through an "external volume", and external engines can read Snowflake-managed Iceberg tables through the Horizon Iceberg REST Catalog API (Snowflake Iceberg documentation, October 2026). Catalog integrations can also read Delta and Parquet files.
One naming note for 2026: Snowflake Open Catalog (its hosted Apache Polaris service) is closed to new customers, who are pointed to Snowflake Horizon Catalog instead (Snowflake documentation, October 2026).
Databricks uses Delta Lake for everything by default and also supports Iceberg as an open format (Databricks product page, October 2026). So the old line "Snowflake is closed, Databricks is open" is out of date. Both now read and write open formats; they just start from different defaults.
Compute
A Snowflake virtual warehouse comes in T-shirt sizes. Credits per hour double at each step: X-Small 1, Small 2, Medium 4, Large 8, X-Large 16, up to 6X-Large at 512 (Snowflake documentation, October 2026). New standard warehouses default to Gen2 (newer hardware and software) in regions where it is available. Adaptive Warehouses, where Snowflake manages sizing and scaling for you, became generally available on AWS on 16 June 2026 and need Enterprise Edition or higher. Multi-cluster warehouses add clusters for more concurrent users, also on Enterprise and above.
Databricks gives you scalable compute clusters for Spark and notebooks, and SQL warehouses for SQL queries (Databricks documentation, October 2026). You choose and configure cluster settings, which is more work but more control.
SQL vs notebooks
Snowflake's home is the SQL worksheet in Snowsight. For Python, Java and Scala you use Snowpark, a library whose DataFrame operations are "lazy": they are built on your machine and sent to Snowflake as SQL only when you ask for a result, so the work runs on Snowflake compute. Snowflake Notebooks add cell-based Python, SQL and Markdown inside Snowsight. Our separate Snowpark in Snowflake guide goes deeper.
Databricks' home is the notebook. Notebooks support Python, R and Scala in addition to SQL, with charts and Markdown in the same document (Databricks documentation, October 2026). If you already write PySpark, you will feel at home.
Governance
Snowflake Horizon Catalog covers role-based access control (RBAC: privileges go to roles, and roles go to users), masking and row access policies (also extended to Iceberg REST engines such as Spark, Flink and Trino), sensitive-data classification, AI Guardrails, Trust Center and column-level lineage (Snowflake Horizon documentation, October 2026). Databricks Unity Catalog provides a unified governance model for the lakehouse, with access controls, lineage, auditing and secure sharing (Databricks documentation, October 2026).
Ease of use
Snowflake has fewer moving parts. Auto-suspend and auto-resume are on by default, so a warehouse stops charging when idle. Databricks asks you to understand Spark, clusters and file layout to get the best results. That is why SQL developers usually get productive on Snowflake faster, while engineers who already know Spark often prefer Databricks' flexibility.
5. Pricing: credits vs DBUs
Short answer: Snowflake charges credits for compute time plus a flat storage rate per TB per month; Databricks charges DBUs for compute, and your cloud provider separately bills the storage and networking in your account.
How Snowflake pricing works
Total Snowflake cost is compute plus storage plus data transfer. Compute is measured in credits: credits used multiplied by the price per credit. Warehouses bill per second, with a 60-second minimum each time they start (Snowflake documentation, October 2026). From 15 October 2026, Snowflake's Service Consumption Table calls compute credits "Platform Credits" and bills AI work separately in "AI Credits".
The same table lists these on-demand list prices for AWS US East (N. Virginia), as of October 2026: $2 per credit on Standard, $3 on Enterprise, $4 on Business Critical and $6 on Virtual Private Snowflake, and $23 per TB per month for on-demand storage (Snowflake Service Consumption Table, October 2026). Prices vary by cloud, region and edition, so always check your own region.
Worked example (illustrative only): a Medium warehouse uses 4 credits per hour. If a nightly ETL job keeps it running 2 hours a day for 30 days, that is 4 x 2 x 30 = 240 credits. At the US East Enterprise list price of $3, compute is 240 x 3 = $720 for the month, before storage. Halve the runtime with better SQL, and you halve the bill. This is why auto-suspend and query tuning matter so much.
How Databricks pricing works
Databricks offers pay-as-you-go pricing with per-second metering and no upfront cost, plus discounts for committed use. A DBU (Databricks Unit) is "a normalized unit of processing power" used to measure and price usage. Databricks pricing covers compute; storage and networking are billed by your cloud provider (Databricks pricing page, October 2026). DBU rates depend on the product, cloud and plan, so we do not quote a single figure here.
Which is cheaper?
No independent benchmark was verified for this guide, so we will not claim either one is cheaper. In practice the cheaper platform is the one your team runs well: right-sized compute, idle compute switched off, and efficient queries. When you compare quotes, compare the full bill. For Snowflake, that is credits plus storage plus transfer. For Databricks, that is DBUs plus your cloud provider's compute, storage and networking charges.
6. AI and machine learning compared
Short answer: Both platforms now offer LLM functions in SQL, natural-language question answering for business users and AI agents; Databricks has deeper roots in classic ML with MLflow, while Snowflake focuses on bringing AI to governed SQL data.
Snowflake
- Cortex AI Functions: SQL functions such as AI_COMPLETE, AI_CLASSIFY, AI_FILTER, AI_AGG and AI_TRANSLATE that call LLMs from providers including OpenAI, Anthropic, Meta, Mistral AI, DeepSeek and xAI inside Snowflake's service perimeter (Snowflake documentation, October 2026). Cortex is Snowflake's name for its managed AI services. Availability varies by region, so check before you plan a project in an India region.
- Cortex Agents: a managed agent platform that plans, uses tools (Cortex Analyst for structured data, Cortex Search for text, custom procedures) and reflects. Generally available since 4 November 2025.
- Snowflake CoWork (formerly Snowflake Intelligence): lets business users ask questions in plain language over structured and unstructured data. It became generally available on 4 November 2025. Our separate Snowflake Intelligence guide covers it in detail.
- Snowflake ML: Feature Store, Model Registry, ML Jobs, Experiments and ML Observability, plus Snowpark Container Services for running containers, including on GPU nodes.
Databricks
- MLflow: an open-source project from Databricks for tracking and managing ML work, with managed integration on the platform.
- Databricks Runtime for ML: compute that comes with ML libraries such as Hugging Face Transformers.
- AI Functions: let SQL analysts call LLMs inside data pipelines.
- Genie: business users explore data in natural language.
- Agent Bricks: Databricks' tooling for building AI agents (Databricks product page, October 2026).
Cortex AI vs Genie: a fair summary
Snowflake CoWork and Databricks Genie aim at the same user, a business person who wants answers without writing SQL. The better choice usually follows where your data already sits and which governance layer your company trusts. If you plan to work as a data scientist building and tracking many models, Databricks' MLflow-centred workflow is the more common path. If you plan to add AI to SQL pipelines and reports, Cortex AI Functions are quick to learn.
7. When to choose Snowflake and when to choose Databricks
Short answer: Choose Snowflake for SQL-heavy analytics, BI, governed sharing and teams with few platform engineers; choose Databricks for Spark-heavy engineering, streaming on lake data and ML-first teams.
Choose Snowflake when
- Your team is mostly SQL developers, analysts and BI developers
- You want a managed platform with little cluster tuning
- You share data with partners: Secure Data Sharing lets a consumer query your data without copying it, and consumers pay only for their compute (Snowflake documentation, October 2026)
- You are migrating an on-premise warehouse such as Teradata, Oracle or SQL Server (one Deloitte Snowflake architect listing in October 2026 asked for migration experience from Teradata, Redshift and SQL Server)
Choose Databricks when
- Your team writes Python and PySpark every day
- You process very large raw files, logs or streaming data in a data lake
- Machine learning and model tracking are central to the work
- You want data to stay as Delta files in your own cloud account
Use cases in the Indian context
These are typical patterns, not rules. Real teams choose on existing skills, contracts and cloud strategy as much as on features.
8. How Snowflake and Databricks work together
Short answer: Many companies use both: Databricks for heavy Spark engineering and ML, Snowflake for SQL analytics and sharing, often connected through shared Apache Iceberg tables or Snowflake's Spark connector.
A common pattern looks like this:
- Land: raw files arrive in cloud storage (S3, ADLS or GCS).
- Engineer in Databricks: Spark jobs clean and join large raw data.
- Store in an open format: curated tables are written as Iceberg tables in the company's own bucket.
- Serve in Snowflake: Snowflake reads those tables through an external catalog or catalog integration, and analysts query them with SQL.
- Govern and share: Snowflake roles, masking policies and Secure Data Sharing control who sees what.
- Report: BI tools and Snowflake CoWork sit on top.
The reverse also works. External engines such as Spark can read Snowflake-managed Iceberg tables through the Horizon Iceberg REST Catalog API, and Snowflake also documents its own Spark connector (Snowflake documentation, October 2026).
Here is how you create an Iceberg table with Snowflake as the catalog, which other engines can then read.
-- Simplified example: Iceberg table with Snowflake as the catalog
CREATE ICEBERG TABLE my_iceberg_table (amount INT)
CATALOG = 'SNOWFLAKE'
EXTERNAL_VOLUME = 'my_external_volume'
BASE_LOCATION = 'my_iceberg_table';
You must create the external volume first, and BASE_LOCATION cannot be changed later (Snowflake documentation, October 2026). Note that customer-managed storage gets no Fail-safe (Snowflake's 7-day, support-only recovery period), so plan your own backups.
For a data engineer, this is the real 2026 skill: not picking a side, but knowing how open formats let both platforms share the same data.
9. Careers and jobs in India
Short answer: Snowflake skills appear in a large number of Indian job listings, mostly in IT services firms and GCCs, and almost every Snowflake job description asks for SQL, with dbt and Python close behind; Databricks counts on job portals are not directly comparable, so do not pick a platform on raw counts.
What job portals showed on 25 October 2026
These are keyword-match counts from a logged-out Naukri search on 25 October 2026. They include duplicates, recruiter re-posts and listings that mention Snowflake as one skill, and city counts overlap, so they are not a count of open Snowflake jobs. LinkedIn showed 6,000+ results for "Snowflake" in India on the same day, with the same caution.
Why no Databricks number in this table? The "databricks" search on Naukri matched in a different way (its first page was dominated by Databricks the company's own listings), so putting the two counts side by side would mislead you. What we can say is that entry-level data roles often list both. For example, a Yotta Techports "Associate Data Forward Deployed Engineer" listing in Hyderabad (0 to 3 years, seen 25 October 2026) named "Snowflake, Databricks or BigQuery" as good to have.
What job descriptions ask for
We read 14 Snowflake job descriptions on 25 October 2026 (Infosys, HCLTech, Capgemini, TCS, Ontimeglobal, Insight, DTCC, Deloitte, EPAM, Caterpillar, Cognizant and others):
- SQL appeared in all 14
- dbt (data build tool, which runs SQL transformations as tested, version-controlled models) appeared in 9
- Python appeared in 9; PySpark appeared in one Hyderabad data engineer listing, and Deloitte's architect role asked for "SQL, Python or Spark"
- SnowPro certification was required in only 1 and preferred in 1
- Experience: none accepted 0 years; the lowest ask was 2 to 5 years
The overlap with Databricks skills is clear: Python, Spark, dbt, cloud (AWS or Azure) and data modelling show up on both sides. For the full role picture, see our Snowflake data engineer guide.
Salary snapshot
AmbitionBox typical ranges, read logged out in October 2026:
Source: AmbitionBox Snowflake Developer and related profile pages, October 2026. The Databricks Data Engineer figure comes from only 54 salaries, so do not conclude that one platform pays more than the other. Titles and samples differ, and pay follows your experience, employer type and city more than the logo on the tool.
10. Certifications compared
Short answer: Snowflake's entry exam is SnowPro Core (COF-C03); Databricks' most common entry exam for engineers is Data Engineer Associate; both are valid for 2 years, and neither is required by most Indian job descriptions we read.
The table draws on Snowflake's SnowPro Core COF-C03 page on learn.snowflake.com (October 2026) and the Databricks Data Engineer Associate page (October 2026).
Beyond the entry level, Snowflake offers Advanced exams such as Data Engineer (DEA-C02) and Architect (ARA-C01), which need an active SnowPro Core. Databricks lists Associate and Professional levels for Data Engineer and ML Engineer, plus Data Analyst, Generative AI Engineer and Apache Spark Developer associate exams (Databricks certification page, October 2026). For a full exam guide, read our SnowPro Core certification post.
Our advice: certify on the platform you actually use in projects. A certificate without hands-on work rarely gets you shortlisted, because only 1 of the 14 JDs we read required SnowPro.
11. Which should you learn first? A guide by background
Short answer: Start with Snowflake if you come from SQL, ETL, BI or DBA work; start with Databricks if you already write Python and Spark; freshers should start with SQL and one platform, then add the other.
A quick note on ELT, which you will meet on both platforms: ELT means Extract, Load, Transform, where you load raw data first and transform it inside the platform with SQL or Spark. Both Snowflake and Databricks are built for ELT.
A simple 8-week path for a fresher
- Weeks 1 to 2: SQL joins, GROUP BY, window functions and CTEs
- Weeks 3 to 4: Snowflake basics: warehouses, stages, COPY INTO, Time Travel, roles
- Week 5: one end-to-end Snowflake project on a public dataset
- Week 6: Python basics and pandas
- Week 7: Databricks notebook basics and a small PySpark job
- Week 8: write Iceberg or Parquet data from one side and read it from the other, then put both projects on GitHub
Be honest with yourself about entry routes: we found no "Snowflake trainee" or "Snowflake intern" titles in October 2026. Freshers usually enter through associate or junior data engineer roles where Snowflake or Databricks is a preferred skill. This plan leaves you able to talk about both platforms in an interview, which is exactly what mixed-stack teams want.
12. Other alternatives: BigQuery, Fabric and more
Short answer: Snowflake and Databricks are not the only options; Google BigQuery, Microsoft Fabric and Amazon Redshift are the other big names, and each fits best inside its own cloud.
- Google BigQuery: Google describes it as serverless with no instances to provision, priced either on demand per TiB processed or by slot capacity (Google Cloud pricing page, October 2026).
- Microsoft Fabric: its Data Warehouse is developed mainly with T-SQL and stores data as Delta tables in OneLake. Microsoft offers a Migration Assistant from Azure Synapse Analytics (Microsoft Learn, October 2026), which answers many "Snowflake vs Databricks vs Synapse" searches.
- Amazon Redshift: AWS's warehouse, with provisioned RA3 clusters or Redshift Serverless. Our Snowflake vs Redshift post compares it in detail.
For the full list, including Oracle, Teradata, ClickHouse and Firebolt, read our Snowflake competitors guide.
A word on market share: we could not verify an independent market-share figure for either company, so we do not quote one. What is on record: Snowflake reported product revenue of $1.49 billion for Q2 of fiscal 2027 (quarter ended 31 July 2026), up 37% year over year, and more than 14,500 customers (Snowflake results release, September 2026). Databricks says it was named a Leader in the 2025 Gartner Magic Quadrant for Cloud Database Management Systems (Databricks blog, November 2025); Gartner does not endorse vendors.
13. How Brolly Academy helps
Short answer: Brolly Academy's Snowflake course gives SQL, ETL and fresher learners a structured 45-day path to hands-on Snowflake skills, and Brolly runs a separate Databricks course if you want both.
If this comparison points you to Snowflake first, the Snowflake Training in Hyderabad course is built for that start:
- Duration and modes: 45 days, in classroom, live online or a self-paced video plan
- Fees (course page, October 2026): Video ₹8,999, Online ₹17,999, Classroom ₹22,999, with EMI options
- Trainer: Mr. Bala Krishna, with 13+ years in data warehousing, SQL and Snowflake
- Syllabus: 15 modules, from architecture and data warehousing concepts to loading, Snowpipe, semi-structured JSON, tasks and streams, secure data sharing, cloning, Time Travel, performance and cost
- Projects: 6 portfolio projects, such as an ETL pipeline, a Customer 360 view and e-commerce analytics
- Certificate: a Brolly Academy course completion certificate; the course prepares you for SnowPro Core, but Snowflake's exam fee is separate
The Snowflake course focuses on Snowflake itself. If you also want the Spark side of the story, Brolly has a separate Databricks course page. Many learners take Snowflake first, build two projects, then add Databricks.
Not sure whether to start with Snowflake or Databricks?
Book a free demo class with Brolly Academy, tell the trainer your background, and get an honest recommendation on which platform to learn first, plus a look at the 45-day Snowflake course, its 6 projects and the current batch timings.
Frequently asked questions
Which is better, Snowflake or Databricks?
Neither is better for every team. Snowflake is stronger for SQL analytics, BI, governed data sharing and teams that want little infrastructure to manage. Databricks is stronger for large-scale Spark engineering, data science and machine learning in notebooks. In 2026 both support Apache Iceberg, both offer SQL compute and both ship AI agents, so the right choice depends on your team's skills, your data and your workloads.
What is the main difference between Snowflake and Databricks?
The main difference is design. Snowflake started as a managed cloud data warehouse with separate storage, compute and cloud services layers, and you mostly use it through SQL. Databricks started as an Apache Spark platform and calls itself a lakehouse, keeping data as Delta Lake files in your own cloud storage and working mainly through notebooks in Python, R, Scala and SQL.
Is Databricks a data warehouse?
Databricks is a lakehouse, which combines a data lake with warehouse features, rather than a classic data warehouse. It offers SQL warehouses, which are compute clusters for running SQL queries from the SQL editor or notebooks, so it can serve warehouse-style workloads. Snowflake, by contrast, began as a cloud data warehouse and later added lake features such as Apache Iceberg tables.
Is Databricks replacing Snowflake?
No. There is no evidence that Databricks is replacing Snowflake. Snowflake reported product revenue of $1.49 billion for Q2 of fiscal 2027, up 37% year over year, and more than 14,500 customers. Both platforms are adding features in each other's space, and many companies use both together, so learning either one remains a sound career choice.
Is Databricks cheaper than Snowflake?
There is no verified, independent answer. Snowflake charges credits for compute plus storage per TB per month, while Databricks charges DBUs for compute and your cloud provider bills storage and networking separately. The cheaper platform is usually the one your team runs well, with right-sized compute, idle compute switched off and efficient queries. Always compare the full bill for your own workload and region.
How does Snowflake pricing work compared with Databricks pricing?
Snowflake bills compute in credits per second, with a 60-second minimum each time a warehouse starts, and storage at a flat rate per TB per month. On-demand list prices for AWS US East in October 2026 were $2 to $6 per credit by edition. Databricks bills DBUs with per-second metering, offers pay-as-you-go and committed-use discounts, and leaves storage to your cloud provider.
What is a DBU in Databricks?
A DBU, or Databricks Unit, is what Databricks calls a normalised unit of processing power, used to measure and price compute usage. You pay for the DBUs your workloads consume, with per-second metering. Storage and networking are not included in DBUs and are billed by your cloud provider. It plays a similar role to a credit in Snowflake, but the two are not directly comparable.
Which is easier to learn, Snowflake or Databricks?
Snowflake is usually easier to learn if you know SQL, because most work happens in SQL worksheets and the platform manages storage layout, auto-suspend and auto-resume for you. Databricks asks you to understand Apache Spark, clusters and file layout to get good results. If you already write Python and PySpark, Databricks may feel more natural to you.
Should a fresher learn Snowflake or Databricks first?
Most freshers should learn SQL first, then Snowflake, because SQL appeared in every Snowflake job description we read and Snowflake has fewer settings to manage. After one end-to-end Snowflake project, add Python and basic Databricks notebooks. Snowflake-titled roles we saw started at about 2 to 5 years' experience, so freshers usually enter through associate or junior data engineer roles.
Which has more jobs in India, Snowflake or Databricks?
Job portal counts cannot answer this fairly. On 25 October 2026, Naukri showed about 25,900 results for "snowflake" across India, but the "databricks" search matched differently, so the two numbers are not comparable. Both are keyword-match counts, not open jobs. Entry-level data listings often name both platforms, so strong SQL and Python skills help on either side.
Snowflake or Databricks for career growth?
Both offer good career growth in India. Snowflake roles are common in IT services firms and GCCs, with AmbitionBox showing ₹10.1 to 11.1 lakh a year for Snowflake Data Engineers with 2 to 9 years' experience in October 2026. Databricks skills suit Spark and ML-heavy roles. The strongest profiles combine SQL, Python, dbt, one cloud and working knowledge of both platforms.
Do Snowflake or Databricks engineers earn more?
The data does not support a clear answer. AmbitionBox showed ₹10.1 to 11.1 lakh a year for Snowflake Data Engineers from 1.3k salaries and ₹10.6 to 11.7 lakh for Databricks Data Engineers from only 54 salaries in October 2026. The Databricks sample is small, so do not conclude that either pays more. Experience, employer type and city matter more than the platform name.
Can Snowflake and Databricks be used together?
Yes, and many companies do. A common pattern is to engineer large raw data with Spark in Databricks, write curated tables in Apache Iceberg format to your own cloud storage, and query them in Snowflake for SQL analytics, sharing and BI. External engines such as Spark can also read Snowflake-managed Iceberg tables through the Horizon Iceberg REST Catalog API.
What is the difference between Delta Lake and Apache Iceberg?
Both are open table formats that store tables as Parquet files plus metadata, adding reliable transactions and history. Delta Lake is the default format for all operations on Databricks and adds ACID transactions and time travel. Apache Iceberg is supported by Snowflake, with Snowflake or an external catalog, and Databricks also supports it. Iceberg is often the shared format when both platforms touch the same data.
Which is better for machine learning, Snowflake or Databricks?
Databricks has deeper roots in machine learning, with MLflow for tracking and managing models, Databricks Runtime for ML and notebooks in Python, R and Scala. Snowflake offers Snowflake ML, including a Feature Store and Model Registry, plus Snowpark Container Services with GPU options. For model-heavy data science, Databricks is the more common choice; for adding AI to SQL pipelines, Snowflake is simpler.
How do Snowflake Cortex AI and Databricks Genie compare?
They target different layers. Cortex AI Functions are SQL functions such as AI_COMPLETE and AI_CLASSIFY that run LLMs inside Snowflake. The closer match to Databricks Genie is Snowflake CoWork, formerly Snowflake Intelligence, where business users ask questions in plain language. Genie lets Databricks users explore data in natural language. The better fit usually follows where your data and governance already live.
Which is better for data engineering, Snowflake or Databricks?
It depends on the pipeline. Snowflake suits SQL-based ELT with stages, COPY INTO, streams, tasks and dbt, and Snowflake job descriptions we read named dbt in 9 of 14. Databricks suits Spark-based processing of very large raw files and streaming data in a lake. Many data engineers in India use both, joined by open formats such as Apache Iceberg.
Does Snowflake support Apache Iceberg?
Yes. Snowflake supports Apache Iceberg tables, either with Snowflake as the catalog, giving full read and write support and automatic maintenance such as compaction, or with an external catalog such as AWS Glue. The data can sit in your own S3, Google Cloud Storage or Azure storage through an external volume. Customer-managed storage gets no Fail-safe, so plan your own backups.
Which certification is better, SnowPro Core or Databricks Data Engineer Associate?
Choose the certification for the platform you use. SnowPro Core COF-C03 has 100 questions in 115 minutes and costs $175, or $140 for exams in India. Databricks Data Engineer Associate has 45 scored questions in 90 minutes and costs $200. Both are valid for 2 years. Only 1 of the 14 Snowflake job descriptions we read required SnowPro, so projects matter more.
Is SQL enough for Snowflake and Databricks?
SQL is the foundation for both and appeared in all 14 Snowflake job descriptions we read in October 2026. It is enough to start on Snowflake, but most roles also want Python, which appeared in 9 of 14, and dbt, also in 9. For Databricks, you will usually need Python and PySpark as well as SQL. Learn SQL deeply first, then add Python.
What is the market share of Snowflake vs Databricks?
We could not verify an independent market-share figure for either company, so this guide does not quote one. Snowflake reported more than 14,500 customers and $1.49 billion in product revenue for Q2 of fiscal 2027. Databricks says it was named a Leader in the 2025 Gartner Magic Quadrant for Cloud Database Management Systems; Gartner does not endorse vendors.
Snowflake vs Databricks vs BigQuery: how do they differ?
Snowflake is a managed SQL-first platform priced in credits and runs on AWS, Azure and Google Cloud. Databricks is a Spark-based lakehouse priced in DBUs on your own cloud storage. Google BigQuery is serverless with no instances to provision, priced on demand per TiB processed or by slot capacity, and fits best for teams already on Google Cloud.
Snowflake vs Databricks vs Synapse: what should Azure teams know?
Snowflake and Databricks both run on Azure, and Snowflake has an Azure Central India region in Pune. Microsoft's current warehouse is Fabric Data Warehouse, developed mainly with T-SQL and storing data as Delta tables in OneLake. Microsoft offers a Migration Assistant from Azure Synapse Analytics to Fabric, so new Azure projects usually compare Snowflake and Databricks with Fabric rather than Synapse.
Does Snowflake have an India region, and where does Databricks run?
Snowflake lists two India regions: AWS Asia Pacific (Mumbai) and Azure Central India (Pune), with no Google Cloud India region. Databricks runs inside your own cloud account and publishes its documentation for AWS, Azure and Google Cloud. Check each vendor's current region list before you plan a project, because AI feature availability also varies by region.
Does the Brolly Academy Snowflake course cover Databricks?
No. The Brolly Academy Snowflake course is a 45-day program focused on Snowflake, with 15 modules and 6 projects covering loading, Snowpipe, JSON, tasks and streams, sharing, Time Travel and performance. Brolly runs a separate Databricks course for learners who want the Spark side too. Many learners take Snowflake first, build projects, and then add Databricks.
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45-day course covering Snowflake architecture, warehouses, data loading, security, performance and real-time projects, in classroom, live online and recorded plans.
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