Databricks vs. Snowflake: A Deep Dive into the Battle for the Modern Data Platform

Databricks and Snowflake are two leading platforms in the data industry, but they serve different needs:

• Databricks: Best for advanced machine learning, real-time analytics, and handling diverse data types (structured, semi-structured, unstructured). Built on Apache Spark, it excels in AI-driven projects but requires technical expertise.
• Snowflake: Simplifies SQL-based analytics and is ideal for structured data and business intelligence. Its fully managed service is user-friendly and scales efficiently for traditional data warehousing.

For Australian businesses, the choice depends on factors like compliance with local regulations, data sovereignty, and specific industry needs. Databricks suits organisations prioritising AI and predictive analytics, while Snowflake works well for SQL-heavy workloads and reporting.

Quick Comparison:

Hiring the right talent is critical for success. Platforms like Talentblocks connect Australian businesses with skilled freelancers who can optimise Databricks or Snowflake for their needs, ensuring cost-effective and compliant implementations.

Snowflake vs. Databricks: A deep dive

What is Databricks

Databricks is a cloud-based platform that brings together data engineering, data science, and machine learning using Apache Spark. By automating infrastructure scaling and optimising performance, it simplifies data processing and model deployment for users.

Operating entirely in the cloud, Databricks supports the entire data lifecycle. It combines the functionalities of data warehouses and data lakes, leveraging generative AI to enhance data semantics and unify these traditionally separate systems. The platform is compatible with AWS, Azure, and Google Cloud, offering flexible multi-cloud scaling.

"Databricks is used for data engineering, data science, and analytics on a unified platform. It enables users to prepare and process data, build machine learning models, and analyze large datasets quickly and efficiently using cloud resources."

Main Features and Benefits

Databricks stands out in the data analytics space by offering a collaborative workspace for data engineers, scientists, and analysts. One of its key features is Delta Lake, which blends the capabilities of data lakes and data warehouses into a single architecture. The platform also integrates advanced tools like MLflow and Spark MLlib, while its optimised Apache Spark engine handles massive datasets with ease.

Australian companies have seen notable benefits from Databricks. For instance, AusNet Services, a major player in energy and infrastructure, reported impressive results after adopting the Databricks Data Intelligence Platform on Azure. They achieved 50% cost savings, 3× faster data processing, and reduced operational overhead by 20–30%.

"Databricks is a robust platform that serves as the backbone in our data transformation journey. It is reliable, efficient and scalable to our needs, allowing effective collaboration between our data engineers, data scientists and business analysts." – Seweryn Golinski, Senior Data Solutions Designer, AusNet Services

With its ability to reduce data silos, speed up insights, and ensure strong security and compliance, Databricks is a valuable tool for businesses looking to maximise their data.

Best Use Cases

Databricks is ideal for large-scale AI and machine learning projects, real-time analytics, and complex ETL pipelines. It’s particularly useful for organisations that need to process vast amounts of data quickly and turn it into actionable insights. Success stories from global companies highlight its impact - Block achieved a 12× reduction in computing costs, Unilever accelerated development by 10×, and Minecraft cut processing times by 66%.

In healthcare, Databricks helps manage and analyse extensive patient datasets, improving diagnostics and outcomes. Financial institutions use it for risk analysis, fraud detection, and customer data management. Retailers rely on the platform to optimise inventory, enhance customer service, and personalise marketing efforts.

The platform also excels in real-time personalisation. For example, Skechers partnered with ActionIQ and Databricks to personalise customer journeys, leading to a 324% increase in click-through rates, a 68% drop in cost-per-click, and a 28% boost in return on ad spend. Similarly, HSBC developed a real-time personalisation engine with Databricks, achieving a 4.5× improvement in mobile app engagement.

Limitations and Requirements

While Databricks offers powerful tools, it requires advanced technical skills to unlock its full potential. Organisations often need to train their teams or hire experts familiar with Apache Spark, distributed computing, and machine learning workflows.

For Australian businesses, compliance with local regulations like the Australian Privacy Principles and data sovereignty rules is essential, especially when working in multi-cloud environments. Additionally, the platform's complexity demands best practices, such as monitoring queries, maintaining data quality, and implementing efficient designs like the Medallion Architecture.

"The Lakehouse paradigm is a new, open standard that combines the best elements of data lakes and data warehouses. This approach enables organisations to perform all types of data workloads on all their data, resulting in meaningful insights and real business outcomes." – Matei Zaharia, the original creator of Apache Spark and CTO of Databricks

Databricks offers unmatched capabilities, but its challenges make it an interesting counterpart to Snowflake’s approach, setting the stage for a deeper comparison.

What is Snowflake

Snowflake is a cloud-based data warehousing platform designed to handle massive amounts of data with ease. It offers a fully managed solution that eliminates the need for hardware or software management, combining a modern SQL query engine with a cloud-native architecture.

"Data is the New Oil, and Snowflake makes it simple to access data globally using a few clicks." – Forbes Magazine

The platform’s architecture is built on three core layers: Database Storage, Query Processing, and Cloud Services. This design allows storage and compute resources to scale independently, ensuring high performance without compromise. Snowflake also supports instant deployment - no hardware installation required. Users can connect to Snowflake through a variety of methods, including a web-based interface, command-line tools, ODBC and JDBC drivers, native connectors, and third-party integrations. Operating seamlessly across AWS, Azure, and Google Cloud, Snowflake provides a unified solution for managing data across platforms.

Main Features and Benefits

Snowflake’s architecture blends elements of shared-disk and shared-nothing designs, enabling effortless data management with the scalability of massively parallel processing. It supports both structured and semi-structured data, making it adaptable to various data needs. Its SQL-based interface is user-friendly, catering to both novice and experienced users.

Performance is a standout feature. Snowflake can process between 6 to 60 million rows of data in just 2 to 10 seconds. By separating compute and storage, organisations can scale resources as needed, balancing performance and cost efficiency. Additionally, the platform’s secure data sharing capabilities allow for governed data exchange across teams and external partners without duplicating data. These features make Snowflake an excellent choice for traditional data warehousing and batch analytics.

Best Use Cases

Snowflake shines in areas like traditional data warehousing, business intelligence, and batch analytics. Its SQL-based interface simplifies complex analytics, making it accessible across industries. For instance, a major bank used Snowflake to integrate various data sources, providing a complete view of customer activities. This improved their risk assessments and fraud detection capabilities.

Other examples highlight its impact. Warner Music Group uses Snowflake to process billions of interaction data points, enabling insights into over 1,000 audience segments. A global bank reported saving over US$100 million in legacy costs after adopting Snowflake. Discover Financial Services streamlined its operations by integrating Snowflake with data governance tools, saving 200,000 hours and reducing pipeline creation times from 30 days to just two. Companies have also reported a 50% reduction in time-to-insight and query speed improvements of up to 40%. In some cases, consolidating data assets with Snowflake led to cost reductions of up to 93%.

Limitations and Requirements

While Snowflake excels at handling structured and semi-structured data, it struggles with unstructured data processing, which is better suited to specialised big data platforms. Its standardised approach works well for traditional data warehousing but may not meet the needs of organisations requiring highly customised data processing or advanced machine learning workflows.

Recognising these strengths and limitations provides a solid foundation for comparing Snowflake with other modern data platforms.

Databricks vs Snowflake: Side-by-Side Comparison

Based on their features and performance, Databricks and Snowflake cater to different needs. Deciding between them depends on your business goals and technical expertise. Here's a quick comparison of their key strengths and capabilities:

Performance and Scalability

Databricks is designed for heavy-duty data science tasks, processing complex workloads at impressive speeds - up to 12× faster than some competitors. Built on Apache Spark, Databricks allows users to customise node and cluster configurations, which is ideal for advanced users. For instance, the platform has been used in sports analytics to deliver near-real-time insights.

On the other hand, Snowflake leverages a combination of shared disk and shared-nothing architectures to ensure efficient scaling. Its ability to decouple storage and compute makes it highly efficient for structured data processing and high-concurrency tasks, all with minimal manual tuning. However, it offers less granular control compared to Databricks. A notable example is San Francisco International Airport, which uses Snowflake to manage diverse data sources for operational improvements and compliance.

Now, let’s dive into how these platforms manage different types of data.

Data Handling Capabilities

Databricks is versatile when it comes to data types, supporting structured, semi-structured, and unstructured data. Its strength lies in streaming data processing and the ability to streamline end-to-end machine learning pipelines. A standout example is Bayer’s ALYCE project, which leverages Databricks for analysing complex datasets while adhering to strict compliance standards.

Snowflake, while also capable of handling structured and semi-structured data, shines in SQL analytics. Using columnar storage and caching, it’s a go-to for traditional data warehousing needs. However, for advanced machine learning, businesses often need to rely on third-party tools . This makes Snowflake ideal for organisations focused on SQL-based analytics rather than machine learning workflows.

Integration options also set these platforms apart.

Integration and Compatibility

Databricks thrives on flexibility, thanks to its open-source foundation and the Apache Spark ecosystem. This makes it highly adaptable for various integrations. For example, Workday partnered with Databricks to develop large language models, showcasing its capability in advanced AI initiatives.

In contrast, Snowflake offers a more controlled ecosystem with pre-built solutions and seamless compatibility with business intelligence tools. This streamlined approach simplifies integration for users who prioritise ease of use over flexibility.

Finally, let’s look at how these platforms address security.

Security and Compliance

Both platforms take security seriously but implement it differently. Snowflake offers multi-layered security with role-based access controls, extending down to row and column levels. It also enforces multi-factor authentication (MFA) by default for new accounts as of October 2024.

Databricks, on the other hand, provides customer-managed encryption keys and transparent key management. While it supports AES-256 encryption and multi-factor authentication, the latter is only required for critical operations. Both platforms can be configured to comply with Australia's Privacy Act 1988, ensuring data protection and regulatory adherence.

Which Platform Suits Your Industry

Choosing the right platform depends heavily on your industry's specific data needs and compliance requirements. In Australia, the cloud hosting and data processing services sector is projected to hit A$3.8 billion by 2025, with an impressive annual growth rate of 9.4% over the past five years. This booming growth makes selecting the best platform a critical decision for staying competitive.

Practical Examples

Financial Services face unique challenges, including real-time fraud detection and meeting regulatory reporting standards. For Australian banks, Databricks offers advanced streaming and machine learning tools that enable real-time fraud prevention. Its ability to handle unstructured data also helps institutions analyse customer interactions, social media sentiment, and market trends, bolstering compliance and performance.

On the other hand, Snowflake is ideal for financial institutions focused on traditional reporting and compliance dashboards. Its SQL-based framework supports the structured data analysis needed for regulatory reports, such as those required by APRA, making it a strong choice for organisations prioritising historical reporting over predictive analytics.

Retail and E-commerce businesses benefit differently from each platform. For instance, fashion retailers can leverage Databricks to analyse customer purchase history, browsing patterns, and social media activity. Its predictive modelling capabilities help create personalised marketing strategies and streamline inventory management through real-time data processing.

Meanwhile, Snowflake appeals to retailers needing simple business intelligence dashboards, sales reports, and customer segmentation tools. Its user-friendly design makes it accessible to marketing teams, even those without deep technical expertise.

Healthcare organisations, dealing with sensitive patient data, require platforms with robust security measures. Databricks offers customer-managed encryption keys and transparent key management, ensuring strong data protection. Its machine learning capabilities also support predictive healthcare analytics, drug research, and modelling patient outcomes. These features highlight how specific industry needs influence platform selection.

Industry-Specific Requirements

Regulatory Compliance varies widely across industries in Australia. For example, APRA-regulated entities must comply with CPS 234 to address cyber threats. Non-compliance can result in severe penalties, including civil or criminal proceedings.

Databricks has achieved IRAP PROTECTED status after assessment by an ASD-endorsed IRAP Assessor. This certification, available in AWS Sydney (ap-southeast-2) and all Azure Australia regions, is essential for organisations with strict government or defence requirements.

Data Residency is another key factor, especially for industries requiring sensitive data to remain within Australia. Both platforms meet local hosting standards, but Snowflake offers built-in multi-layered security, including role-based access controls down to row and column levels. Databricks, with its open architecture, allows businesses to implement custom security protocols for more granular control.

Cost Considerations also play a major role. Databricks’ flexible pay-as-you-go pricing model is often more economical for workloads that vary over time. In contrast, Snowflake’s fixed pricing based on pre-allocated resources can lead to over-provisioning and higher costs. For Australian businesses managing seasonal demand fluctuations, this distinction is crucial.

Technical Expertise requirements further influence platform adoption. Snowflake’s intuitive interface is well-suited for organisations with limited data science capabilities, making it popular among traditional enterprises. Databricks, while requiring more technical know-how, offers greater flexibility for businesses with dedicated data science teams.

For healthcare organisations, keeping sensitive data within Australia is critical to reduce risks of unauthorised access. Violations of HIPAA regulations, for example, can result in fines of up to A$1.5 million annually for multiple breaches. This makes robust security features an absolute necessity for providers.

Ultimately, the choice comes down to your industry’s priorities. Snowflake is a strong fit for those focusing on ease of use and traditional analytics, while Databricks excels in advanced machine learning and flexible data processing. Australian businesses must weigh these technical features alongside compliance obligations and cost factors to make the best decision. By doing so, they can extract the maximum value from their chosen platform.

Hiring Freelancers for Databricks and Snowflake Projects

Implementing platforms like Databricks or Snowflake demands a level of expertise that many businesses may not have in-house. With tech skills expected to be in even greater demand by 2025, and companies racing to advance their digital transformation efforts, finding the right talent has become critical. In Australia, the growing demand for skilled tech professionals has made freelancers an appealing solution. They offer access to top-tier expertise without the long-term commitment of full-time hires, aligning perfectly with the fast-evolving digital transformation landscape.

Why Freelancers Are a Great Fit for Data Projects

Freelancers bring immediate value to projects involving data platforms. Their specialised experience - often gained from working on multiple implementations - allows them to hit the ground running. They’ve encountered and navigated common challenges, and they know how to apply best practices to optimise both performance and cost.

Another advantage is flexibility. Data projects often have fluctuating demands, with intense work during implementation phases followed by quieter maintenance periods. Freelancers provide the ability to scale up or down as needed, which can be more cost-effective than hiring full-time employees.

Freelancers also bring diverse perspectives from working across industries. This breadth of experience can inspire fresh approaches and help businesses sidestep mistakes, speeding up the process of getting the most out of platforms like Databricks and Snowflake.

How Talentblocks Simplifies Hiring in Australia

Talentblocks makes it easier for Australian businesses to connect with expert freelancers. The platform’s precise skill filters and rigorous validation process ensure that companies can find professionals with verified expertise in Databricks, Snowflake, and related technologies. Instead of wading through generic profiles, businesses can focus on specialists who meet their exact needs, including platform, cloud provider, and industry-specific requirements.

Talentblocks also simplifies budgeting. By displaying transparent hourly rates in AUD, the platform eliminates currency conversion hassles and makes it easy for businesses to compare costs and plan their projects. Flexible booking options further enhance convenience, whether a company needs short-term consulting or longer project-based work. Plus, scheduling tools ensure smooth collaboration by aligning freelancers with Australian time zones.

For businesses new to data platforms, Talentblocks offers tailored recommendations through its wizard tool or personal consultations. This guidance helps companies identify the specific skills and expertise they need, even if they’re unsure of the technical details. Administrative tasks like timesheet approvals and payment processing are also streamlined, allowing businesses to focus on project outcomes.

Key Skills for Databricks and Snowflake Projects

The technical requirements of Databricks and Snowflake projects highlight the importance of hiring freelancers with proven expertise. Here’s what to look for:

• SQL Mastery: Strong SQL skills are a must for both platforms, especially Snowflake, where complex queries and performance optimisation are critical. Freelancers should be adept with advanced SQL concepts, such as window functions, CTEs, and query tuning.
• Data Warehousing Knowledge: A solid grasp of data warehousing principles, including star and snowflake schemas, is essential for designing efficient data models. Familiarity with dimensional modelling is a plus.
• ETL/ELT Expertise: Proficiency in tools like Talend, Fivetran, or Matillion is key, as modern data projects rely heavily on automated pipelines.
• Cloud Computing Skills: Experience with AWS, Azure, and Google Cloud enables freelancers to design solutions that maximise the strengths of each platform. A strong understanding of cloud architecture is vital for balancing cost and performance.
• Programming Proficiency: For Snowflake projects, knowledge of Python and Snowpark is crucial for advanced data transformations and automation. For Databricks, expertise in SparkSQL or PySpark - particularly within Azure-based Data Lakehouse environments - is essential.
• Security and Governance: Freelancers must understand role-based access control, encryption, and data governance practices to ensure robust security.
• Performance Tuning: Skills in query optimisation, clustering, and indexing are critical for efficient large-scale data processing.
• Complementary Tools: Familiarity with Tableau, Power BI, and basic machine learning allows freelancers to contribute across the entire data analytics pipeline.

Australian businesses especially value freelancers who combine technical expertise with adaptability. By focusing on these skills during the hiring process, companies can ensure they’re bringing on board professionals who will deliver real results for their data platforms. Whether it’s through innovative solutions or efficient implementation, the right freelancer can make a significant difference.

Conclusion

Choosing between Databricks and Snowflake ultimately depends on your specific data challenges and technical capabilities.

Databricks stands out as a unified analytics platform. It supports multiple programming languages and processes data up to 12× faster. Its Platform-as-a-Service (PaaS) model allows for handling diverse data types - from raw logs to videos - making it a strong contender for complex analytics and AI-driven projects.

Snowflake, on the other hand, is a cloud-based data warehousing solution that excels in managing structured and semi-structured data using SQL. Its ability to separate storage and compute costs can be especially cost-effective for Australian businesses with predictable workloads.

Beyond technical features, the choice often comes down to how the platform aligns with your use case. For example, Australian companies prioritising business intelligence and straightforward data warehousing - like nib group, which uses Snowflake to compute KPIs in Tableau - may find Snowflake's simplicity and scalability appealing. Meanwhile, organisations focusing on machine learning and advanced analytics, such as Bayer’s ALYCE platform for clinical data analysis, might benefit more from Databricks' advanced machine learning capabilities.

Given the distinct technical demands of each platform, hiring specialised talent becomes essential. Databricks often requires hands-on administration and query optimisation, while Snowflake’s managed service reduces the technical burden. This is where hiring skilled freelancers through Talentblocks can be a game-changer for Australian businesses.

With Australia's tech market on the rise, Talentblocks offers an efficient way to scale your data team. Their AUD pricing and alignment with local time zones make it easier to find the right expertise without committing to full-time hires. By tapping into freelance professionals who understand both the technology and your business needs, you can maximise the potential of either platform and ensure a seamless implementation.

Ultimately, the success of these platforms lies in how well they’re tailored and optimised to meet your organisation's unique goals.

FAQs

Which platform is better for my business: Databricks or Snowflake?

Choosing between Databricks and Snowflake really comes down to what your business needs from its data platform and where you're headed in the long run.

If you're after a data warehousing solution that's easy to set up and use, Snowflake could be the way to go. It’s designed for teams looking for a straightforward, cost-efficient option that doesn’t demand a lot of technical know-how.

On the flip side, Databricks shines when it comes to machine learning, AI, and tackling more complex data engineering challenges. It’s a better fit for organisations with a skilled technical team that needs advanced tools for data science and ETL workflows.

To decide, think about your team’s expertise, the complexity of your data projects, and how your organisation plans to manage and use data in the future.

What key technical skills are needed to work effectively with Databricks and Snowflake?

To work efficiently with Databricks and Snowflake, you'll need to be well-versed in SQL and programming languages like Python, Scala, or R. Since Apache Spark powers Databricks, understanding its role in big data processing is a must. For Snowflake, having a clear grasp of data warehousing concepts will make navigating the platform much easier.

It’s also important to be familiar with ETL/ELT processes, as well as cloud computing platforms like AWS, Azure, or Google Cloud. Knowing the ins and outs of data engineering techniques will help you manage and transform data effectively. Lastly, being skilled in data security and understanding best practices for handling sensitive information is critical, especially when working with these tools in business settings.

What are the advantages of hiring freelancers for Databricks or Snowflake projects?

Hiring freelancers for Databricks or Snowflake projects can be a smart choice for businesses aiming to achieve efficient, high-quality results. These professionals bring deep expertise in areas like data engineering, analytics, and AI, which can lead to quicker project completion and better outcomes. Their skills can streamline workflows, reveal valuable insights, and support more informed decision-making.

Another advantage of freelancers is their flexibility and cost-efficiency. They give businesses access to top-tier talent without the need for permanent hires, making them ideal for short-term or clearly defined projects. This approach allows organisations to adjust resources based on project needs while keeping budgets under control.