Databricks vs Snowflake vs Redshift

Databricks is a modern AI and data analytics platform built on top of Apache Spark that can effectively manage large data workloads. Databricks professional services presents the Lakehouse architecture, which enables businesses to handle both structured and unstructured data within a single, scalable framework by combining the best aspects of data lakes and data warehouses.

A totally cloud-based data warehouse, Snowflake is built for flexible resource management, high-performance SQL analytics, and simple scalability. Snowflake, which offers seamless multi-cloud support across AWS, Azure, and Google Cloud, is designed for the cloud from the ground up, in contrast to traditional data warehouses.

A fully managed cloud-based data warehouse designed for fast SQL analytics on large datasets is Amazon Redshift. It is an effective choice for companies that are currently using the AWS ecosystem because of its close collaboration with AWS and optimization for OLAP (Online Analytical Processing) workloads.

A data platform’s performance, scalability, and use cases are determined by its underlying architecture and technology. The foundations of Databricks, Snowflake, and Redshift are essentially distinct methods of data processing and analytics, each designed for workloads.

• Apache Spark is an open-source distributed computing platform that makes it possible to handle large amounts of data quickly and in memory. Databricks can effectively manage real-time streaming (via Structured Streaming) and batch processing (ETL) due to Spark.
• On top of data lakes (such as AWS S3, Azure Data Lake, and Google Cloud Storage), Delta Lake is a transactional storage layer. Data lakes become more dependable and warehouse-like with Delta Lake’s addition of ACID transactions, schema enforcement, time travel, and data versioning.
• MLflow: Databricks’ integrated end-to-end machine learning lifecycle management platform. Because it facilitates model training, versioning, deployment, and monitoring, Databricks is a preferred platform for workloads including AI and ML

• Multi-Cluster Shared Data Architecture: Compared to conventional databases, Snowflake uses a highly optimized columnar structure to store data independently from compute resources, allowing for elastic scaling and effective query performance.
• Snowflake employs “virtual warehouses” (also known as compute engines) to provide computational power, which allows queries to execute independently without affecting other workloads. To cut expenses, these virtual warehouses can pause when not in use or scale up or down.
• Snowpark is a framework that eliminates the need for additional processing engines by allowing Python, Java, and Scala developers to run custom apps and ML algorithms directly within Snowflake.

• With Massively Parallel Processing (MPP) Redshift allows for quick, scalable analytics by dividing up data and query execution among several cluster nodes.
• Columnar Storage Format: This format reduces input/output and improves query efficiency by using columnar data storage rather than conventional row-based storage.
• RA3 Instances & Managed Storage: AWS Redshift’s RA3 node type improves scalability and cost-efficiency by separating computing and storage.
• Materialized Views & Query Caching: AWS Redshift minimizes the need for recurrent operations by precalculating results to optimize queries.

Performance has a direct impact on query speed, data processing efficiency, and overall system responsiveness, making it a crucial consideration when choosing a cloud data platform. Let’s explore the differences between Databricks vs Snowflake vs Redshift regarding efficiency, workload optimization, and performance.

By indexing, caching, and automatically optimizing queries, Databricks’ Delta Lake maximizes data pools and eliminates the need for complete table scans. Adaptive Query Execution (AQE) enhances query performance by dynamically optimizing query plans according to runtime metrics. Databricks is the go-to option for data scientists and machine learning engineers because it comes with built-in support for Python, Scala, R, and SQL.

For typical ad hoc SQL queries, Databricks could not be as effective as Snowflake since Spark tasks add overhead.

Snowflake dynamically scales resources in response to query demands, the separation of computation and storage guarantees constant performance. When workloads are heavy, it automatically scales up computational resources; when it is not needed, it scales down. However, Snowflake is less effective for AI/ML or unstructured workloads because it is made for structured data and SQL-based analytics.

Redshift speeds up repeated searches by storing prior results and distributing large queries across multiple nodes for parallel processing. Instead of loading data into Redshift first, Amazon Redshift Spectrum for External Queries allows you to query data straight from S3. However, unlike Snowflake, Redshift does not scale storage and computation independently, which may result in performance loss under heavy workloads.

Cost plays a crucial role in selecting a cloud data platform. While performance is important, companies also need to optimize their cloud spending without sacrificing efficiency. Databricks, Snowflake, and Redshift follow different pricing models.

Databricks cost is based on computing resources (DBUs, or Databricks Units) and storage usage in Databricks’ pay-as-you-go business model.

• Compute costs: Every cloud provider (AWS, Azure, and GCP) has a distinct DBU rate, hence pricing varies. Only the resources utilized during active compute sessions are charged to you.
• Storage expenses: By reducing data duplication and improving query efficiency, Delta Lake enhancements assist to lower storage expenses

Snowflake cost has a consumption-based, pay-as-you-use business model in which storage and computation are charged independently.

• Compute costs: assessed on a per-second basis. By allowing users to scale computation up or down at any time, excessive spending can be avoided.
• Storage expenses: billed monthly per TB and are not included in compute charges. The amount of space required is reduced because storage is automatically compressed. However, fail-safe storage (used for data recovery beyond Time Travel retention) comes with an additional cost.

Because Redshift cost uses a node-based pricing mechanism, prices are determined by the kind and quantity of nodes that are utilized. For those that use AWS frequently, it is among the most affordable choices, particularly when using reserved instances.

• Compute costs: Depending on the instance type, pay hourly for each node. Compute expenses can be decreased by using RA3 Nodes, which enable distinct scaling of computation and storage.
• Storage costs: Redshift Spectrum lowers storage costs by enabling direct querying of data on S3. Certain node types (Dense Storage nodes) come with storage, which lowers the cost per TB compared to Snowflake.

When choosing a data platform, security and compliance are crucial factors, particularly for businesses handling sensitive data, financial records, medical data, or personally identifiable information (PII). Strict access controls, encryption standards, and regulatory compliance requirements such as GDPR, HIPAA, SOC 2, and PCI DSS must all be followed by an organization’s data platform.

Although all three platforms- Databricks, Snowflake, and Redshift have robust security features, their governance capabilities, encryption approaches, and access controls vary.

• Authentication & Access Control: Role-Based Access Control (RBAC), which permits fine-grained permissions at the workspace, notebook, and cluster levels, guarantees strong security. While Attribute-Based Access Control (ABAC) dynamically limits access based on user attributes like department, project, or region, Single Sign-On (SSO) and Multi-Factor Authentication (MFA) improve login security.
• Data Encryption & Network Security: Utilizes TLS 1.2 encryption for safe data transit and AES-256 encryption for data at rest to protect data at every stage. PrivateLink and IP whitelisting ensure private, VPN-based connectivity by blocking unwanted access. Secure cluster connectivity and runtime isolation are two runtime security improvements that reduce vulnerability to external threats.
• Compliance & Governance: Ensures regulatory compliance by supporting industry-leading standards such as GDPR, HIPAA, SOC 2, PCI DSS, ISO 27001, and FedRAMP. Businesses can safeguard sensitive data and keep complete visibility into data access and usage by using Unity Catalog’s centralized governance features, which include audit logging, metadata management, column-level protection, and dynamic data masking.

• Authentication & Access Control: RBAC which Snowflake uses to provide granular access control, permits hierarchical privileges across databases, schemas, tables, and views. Security is improved via SSO and MFA connections with enterprise identity providers (Okta, Azure AD, Ping Identity). Row-Level & Column-Level Security features Row Access Policies to limit visibility according to user attributes and Dynamic Data Masking (DDM) to secure sensitive data.
• Data Encryption & Network Security: Uses TLS encryption for safe data transfer and always-on AES-256 encryption for data at rest. Secure data sharing ensures real-time, controlled access and permits cross-account cooperation without transferring or replicating data. Network security is further improved by private connectivity choices including VPC peering, IP whitelisting, AWS PrivateLink, and Azure Private Link.
• Compliance & Governance: To ensure regulatory alignment, Snowflake complies with important compliance standards including GDPR, HIPAA, SOC 2, PCI DSS, ISO 27001, FedRAMP, and CCPA. Complete insight into data exchanges is made possible by integrated audit logging and access tracking. Machine learning is used in Automated Governance with Data Classification to find, tag, and protect sensitive data on a large scale.

• Authentication & Access Control: Redshift supports federated authentication with AWS SSO and uses AWS IAM-based role management to provide fine-grained user rights. GRANT and REVOKE SQL commands are used to manage access control at the database and schema levels, allowing group-based policies. However, custom implementations are needed for data access limitations because native Row-Level Security (RLS) and Dynamic Masking are not included.
• Data Encryption & Network Security: Utilizes TLS encryption for safe data transfers and AWS Key Management Service (KMS) for encryption keys to guarantee data encryption both in transit and at rest. Organizations can operate Redshift in a private AWS Virtual Private Cloud (VPC) for limited access thanks to VPC-based security, which improves network isolation.
• Compliance & Governance: Redshift guarantees enterprise-grade security and regulatory compatibility by adhering to key compliance frameworks such as GDPR, HIPAA, FedRAMP, SOC 1/2/3, ISO 27001, and PCI DSS. All user access and query activity are tracked by thorough audit logging by AWS CloudTrail, facilitating security monitoring and forensic analysis.

The workload type, financial concerns, and business use case all play a role in selecting Databricks, Snowflake, and Redshift as your cloud data platform. From big data processing to SQL-based analytics and AWS-native warehousing, each platform is a leader in specific areas.

Need expert guidance to choose between Databricks vs Snowflake vs Redshift? Let’s discuss your specific use case, workload demands, and budget to determine the best platform for your business. Contact our Data experts today.