A data warehouse is crucial for your organization, irrespective of the industry to which it belongs. These data storage solutions allow you to process large volumes of data from multiple sources in near real-time and derive information about upcoming market trends. This helps you make better business decisions and improve overall operational efficacy.
However, conventional data warehouses are less flexible when it comes to changing data requirements and can be difficult to integrate with other systems. This is where modern solutions, like Yellowbrick, come into the picture. The article offers an in-depth overview of Yellowbrick, its pros and cons, and how it works. It provides you with sufficient information to decide if the tool is a good fit for your specific use case.
Overview of Yellowbrick
Yellowbrick data warehouse is a cloud-native, massively parallel processing (MPP) SQL data platform. Its fully elastic clusters, with separate storage and computing, can help you handle batch, real-time, ad hoc, and mixed workloads. You can use Yellowbrick to perform petabyte-scale data processing with sub-second response times.
The Yellowbrick SQL database can be deployed on-premises, in the cloud (AWS, Azure, Google Cloud), or at the network edge. The platform ensures data protection and compliance while giving you complete control over your data assets. Additionally, Yellowbrick delivers a SaaS-like management experience and runs on Kubernetes, enabling you to implement data operations effortlessly across any environment.
Key Features of Yellowbrick
Yellowbrick offers robust features that make it an ideal option in modern data warehousing. Some of its key features are mentioned below:
- Virtual Compute Clusters: These clusters let you write and execute SQL queries within the system. They also allow you to isolate workloads and allocate computational resources dynamically, facilitating scalability and high concurrency without interference.
- Pattern Compiler: Yellowbrick utilizes a unique compilation framework, the pattern compiler, to improve the execution speed of regular expressions and LIKE operations for large datasets. Currently, the pattern compiler supports input patterns such as SQL LIKE, SQL SIMILAR TO, POSIX-compatible regular expressions, and date/time parsing.
- Code Caching: Yellowbrick’s compiler employs several caching layers to handle dependencies, such as execution engine templates, library versions, and query plans. The platform considers all these dependencies and maximizes the reuse of previously compiled object code, optimizing performance across queries.
- High Availability and Business Continuity: The platform has no single points of failure and is resilient to storage, server, and network outages. Yellowbrick provides incremental, full, and cumulative backups to restore data during data loss or corruption. It also has a built-in asynchronous replication feature that supports failover and failback, ensuring continuous data access and minimal downtime.
- Data Migration: You can easily transition from legacy systems using Yellowbrick’s automated migration suite powered by Next Pathway’s SHIFT. The tool’s unique distributed data cloud architecture allows you to stage cloud migration with minimal risk.
The Architecture of Yellowbrick
Yellowbrick’s architecture is designed for high speed, scalability, and performance. It implements a Massively Parallel Processing (MPP) architecture, where large data workloads are distributed across multiple nodes, and queries are processed in parallel. This enables the Yellowbrick data warehouse to handle complex queries and large datasets swiftly, significantly reducing query processing time.
Another key component of Yellowbrick’s architecture is a combination of innovative hardware (NVMe and Flash memory) and software (Kubernetes) optimization. Flash storage eliminates I/O bottlenecks typically associated with conventional disk-based storage systems. It also allows faster data retrieval and processing. Furthermore, Yellowbrick integrates advanced data compression techniques that reduce the required storage space.
The warehouse also includes a hybrid storage engine that helps you scale your workflows on-premises and in cloud environments. You can easily integrate Yellowbrick with your existing data tools and processes due to its SQL interface and compatibility with PostgreSQL. Additionally, its low-latency performance lets you utilize real-time analytics and reporting.
Use Cases of Yellowbrick
Yellowbrick SQL database has several use cases across industries. Some of them are listed below:
Banking Institutions
With Yellowbrick, bank portfolio managers can perform rapid analytics and make accurate predictions, all while effectively managing costs. They can also conduct complex simulations without any downtime.
Risk management executives can readily execute ad hoc queries or generate reports to assess a client’s or an organization’s risk tolerance. They can quickly identify and prevent fraud in real-time by using sub-second analysis.
Retail Stores
Retailers can leverage Yellowbrick to gain faster, high-quality insights into customer behavior, personalize experiences, and optimize pricing, marketing, and inventory management. It enables them to implement real-time predictive analytics to prevent stockouts and overstocks, reduce unnecessary expenses, and enhance operational efficiency.
Additionally, Yellowbrick allows retailers to monitor supply chains and product distribution and gauge the effectiveness of trade promotions. All these facilities help make informed decisions and increase Return on Investment (ROI).
Telecom Industry
Yellowbrick lets telecom companies streamline operations like billing, customer retention, and network optimization by providing IoT and deeper historical data analytics. The platform offers them the ability to capture billions of call data records (CDRs) and enrich them with additional data sources for detailed analysis. Telecoms can also use Yellowbrick to detect fraud and improve infrastructure management.
Advantages of Using Yellowbrick
- Optimized Storage: Yellowbrick has a hybrid row-column store. The column store utilizes vectorized data compression and smart caching and stores data in object storage for efficacy. On the other hand, the row store processes streaming inserts from tools like Airbyte, Informatica, Kafka, and other data solutions in microseconds.
- Interoperability: The platform resembles PostgreSQL and extends its SQL capabilities to ensure compatibility with Redshift, Teradata, SQL Server, Oracle, and other databases. You can also integrate it with several commercial and open-source CDC, BI, analytics, and ETL tools for interoperability.
- Streamlined Migration: Yellowbrick simplifies legacy database migrations through automated tooling and strategic partnerships with systems integrators, Datometry, and Next Pathway. The tool provides migration services, including thorough environment assessments, cost analysis, testing, and post-migration support.
- Data Security and Compliance: The warehouse includes robust security features such as Kerberos, Role-Based Access Control (RBAC), OAuth2, LDAP authentication, and customer-managed encryption keys. Furthermore, Yellowbrick ensures compliance with FIPS standards, employs TLS encryption, and provides regular monthly vulnerability updates.
Disadvantages of Using Yellowbrick DB
- Limited Vendor Ecosystem: Yellowbrick offers integration with major cloud platforms such as AWS, Azure, and Google Cloud. However, its catalog of third-party tools and integrations is not as extensive as other well-established data warehouses like Snowflake or Redshift. This may limit some flexibility if you work with niche data tools or services.
- Customization Constraints: The platform offers a SaaS-like experience and ease of use, but this simplicity can come at the cost of customization options. If your organization has unique use cases, Yellowbrick’s level of customization might be limited compared to solutions like Apache Spark or Google BigQuery.
- Steeper Learning Curve: While Yellowbrick supports standard SQL, you might find it difficult to implement some of its advanced features, especially in hybrid deployments. This complexity can increase further if your organization has convoluted data environments.
Final Thoughts
Yellowbrick data warehouse is a powerful solution if your organization deals with large-scale, complex data processing tasks. Its massively parallel processing (MPP) architecture allows you to achieve scalability and high-performance analytics for various use cases.
With features like virtual compute clusters, code caching, and robust security, Yellowbrick is your all-in-one platform for real-time analytics, data migration, and business continuity. While it may have some limitations when it comes to third-party integrations, it is still one of the best tools for modern data warehousing.
FAQs
Can Yellowbrick be integrated with third-party BI tools?
Yes, you can integrate Yellowbrick with popular business intelligence (BI) tools such as Tableau, Power BI, and Looker.
What kind of workloads is Yellowbrick suitable for?
Yellowbrick data warehouse is designed for high-performance analytical workloads like complex queries, real-time analytics, and big data processing. It is ideal for industries that require fast, large-scale data handling, like finance, supply chains, and telecommunications.
What is the difference between Snowflake and Yellowbrick?
Snowflake is a cloud-based data warehouse, while Yellowbrick is a data warehousing platform that can be deployed both on-premises and in the cloud.
