PowerLake™

Data Lake Solution

What is a Data Lake?

A data lake is a centralized repository that allows organizations to store, manage, and analyze vast amounts of structured, semi-structured, and unstructured data at scale. Unlike traditional databases, data lakes enable the storage of raw data in its native format, making it easier to accommodate diverse data types and sources.

What is Our Data Lake Solution?

Our data lake solution leverages the medallion architecture, effectively combining the strengths of both data lakes and data warehouses. This innovative approach is not only highly scalable and efficient but also accommodates a diverse range of raw and processed data sets, ensuring rapid access for business users and empowering them to derive advanced insights for informed decision-making. By eliminating data silos, our solution fosters seamless and secure access for authorized users across the organization, all within a unified platform. Built on the robust capabilities of DataCanva, this solution provides a flexible, cloud-neutral framework designed for effortless integration with leading cloud platforms such as Microsoft Azure, AWS, and Google Cloud. This versatility empowers organizations to capitalize on their preferred cloud environments while ensuring comprehensive data management and governance capabilities, ultimately enhancing operational efficiency and strategic agility.

What is Medallion Architecture?

Medallion architecture is a systematic data design pattern that structures data within a data lake. It focuses on progressively improving the organization and quality of data as it transitions through three layers: Bronze, Silver, and Gold.

Building Data Pipelines with Medallion Architecture

DataCanva provides users with robust tools, like Data Pipeline, that facilitate the swift creation of data pipelines utilizing Bronze, Silver, and Gold tables with minimal coding. By leveraging tables and materialized views, users can establish data pipelines based on Structured Streaming, enabling incremental refreshes and updates.

Bronze Layer (Raw Data)

The Bronze layer serves as the initial stage, where all incoming data from external sources is gathered. The table structures here replicate those of the source systems, supplemented by metadata columns that capture crucial details such as load date/time and process ID. This layer prioritizes rapid Change Data Capture (CDC) and functions as a historical repository, supporting data lineage, auditability, and the ability to reprocess data without re-reading from the source systems.

Silver Layer (Cleansed and Conformed Data)

In the Silver layer, raw data from the Bronze layer is refined through matching, merging, conforming, and cleansing—applying just the necessary transformations. This results in an "Enterprise view" of essential business entities, including master records for customers and stores, deduplicated transactions, and cross-reference tables. The Silver layer consolidates data from multiple sources, enabling self-service analytics for ad-hoc reporting, advanced analytics, and machine learning. It serves as a foundational resource for Departmental Analysts, Data Engineers, and Data Scientists, who can create analyses and projects that address business challenges in the Gold layer.
In this data engineering model, an ELT (Extract, Load, Transform) approach is generally preferred over ETL (Extract, Transform, Load). This approach entails minimal transformations during the loading of the Silver layer, emphasizing speed and efficiency in data ingestion. More complex transformations and business rules are applied when transitioning data from Silver to Gold. From a data modeling perspective, the Silver layer often employs structures similar to 3rd Normal Form, while Data Vault-like, write-optimized models can also be utilized.

Gold Layer (Curated Business-Level Tables)

The Gold layer organizes data into consumption-ready, project-specific databases that are optimized for reporting. Here, data models are more denormalized and read-optimized, with fewer joins. This layer is where final transformations and data quality rules are implemented, supporting various analytical projects such as Customer Analytics, Product Quality Analytics, Inventory Analytics, Customer Segmentation, and Product Recommendations. Common data modeling techniques in the Gold layer include Kimball-style star schemas and Inmon-style data marts.

Linking Data Lake with DataCanva

Building a data lake on top of DataCanva includes below processes:

Establish a Clear Data Lake Strategy

Before initiating your data lake project, it's crucial to define a comprehensive strategy. This plan should outline the objectives of your data lake, the types of data to be stored, and the tools and services that will be employed for its management. DataCanva provides several services that facilitate building a data lake, including Dataware House for storage and ETL, and Smart Query for data querying and analysis.

Data Ingestion

The first step in building a medallion architecture data lake is ingesting data from various sources. DataCanva offers multiple options for data ingestion, such as Data Pipeline, Database Migration Service (DMS) for large-scale offline data transfer. Choose the appropriate service based on your specific use case and data source.

Data Storage

DataCanva is a highly scalable and durable object storage service that is commonly used as the primary storage layer for a data lake. It provides a simple interface to store and retrieve any amount of data at any time. Use DataCanva features like versioning, lifecycle policies, and encryption to enhance data security and governance. Additionally, consider partitioning and organizing data within DataCanva based on the intended use cases for better data management and query performance.

Data Cataloging and Metadata Management

DataCanva is a fully managed extract, transform, load (ETL) service that plays a crucial role in cataloging and organizing data in a data lake. It automatically discovers and catalogs metadata about the data assets stored in various sources, making it easy to search, query, and analyze the data. Leverage Website crawlers to extract schema information and maintain an up-to-date data catalog.

Data Transformation and Preparation

Once the data is cataloged, data can be used for data transformation and preparation. It offers a visual interface for creating ETL jobs or writing custom scripts in Python or Scala using Apache Spark. These transformations can help standardize data formats, clean and filter data, and perform aggregations or joins before loading the processed data into the data lake.

Data Governance and Security

Data governance is essential for maintaining data quality, compliance, and security. Ongoing monitoring of data lakes is vital to ensure optimal performance. Keep an eye on performance metrics, security protocols, and compliance standards to address any potential issues promptly. DataCanva provides various security and governance features to protect your data lake, such as encryption at rest and in transit, access control through DataCanva Identity and Access Management (IAM) policies, and integration with Cloud Trail for auditing. Implementing data lake governance best practices ensures data privacy, compliance with regulations, and proper data access controls.

Data Analytics and Exploration

DataCanva offers multiple services to perform analytics and exploration on the data lake. DataCanva provides a serverless query service that enables ad-hoc SQL queries directly on the data stored in DataCanva, making it easy to derive insights. For more complex analytical workloads, DataCanva can be used as a massively scalable data warehouse solution.
With the above steps, a fully functional data lake solution will be supported by the capacities of Datacava, allowing the medallion architecture data lake solution to be easily integrated into the client's existing data platforms.

Benefits of Our Data Lake Solution

Flexibility and Scalability

PowerLake™ can store vast amounts of structured, semi-structured, and unstructured data, allowing organizations to scale storage and processing capabilities as data needs grow.

Cost-Effectiveness

Using commodity hardware and cloud storage solutions, it provides a cost-effective way to store large volumes of data compared to traditional data warehouses.

Diverse Data Integration

It can ingest data from various sources, including IoT devices, social media, and enterprise applications, enabling a comprehensive view of business operations.

Rapid Data Ingestion

With a schema-on-read approach, data can be ingested quickly without the need for extensive preprocessing. This accelerates the availability of data for analysis.

Analytics and Machine Learning

This solution supports advanced analytics and machine learning by providing a rich repository of data. Analysts and data scientists can explore and experiment with data at scale.

Enhanced Data Discovery

Powerful search and discovery tools enable users to find and access relevant data easily, fostering a culture of data-driven decision-making.

Support for Real-Time Processing

Medallion Architecture Data Lake Solution can support real-time data processing through technologies like Apache Spark and Kafka, enabling timely insights and actions based on current data.

Data Governance and Security

Modern data lake architectures often include robust governance frameworks to ensure data quality, compliance, and security, helping organizations manage their data assets effectively.

Facilitates Collaboration

A centralized data repository promotes collaboration among different teams, such as data engineering, data science, and business analytics, by providing a single source of truth.

Historical Data Retention

Data lakes can retain historical data for long periods, allowing organizations to perform trend analysis and gain insights from historical patterns.

Support for Diverse Use Cases

From business intelligence to predictive analytics and data exploration, data lakes enable a variety of use cases, making them suitable for different business needs.

Integration with Existing Systems

Data lakes can complement existing data warehouses and traditional databases, allowing organizations to leverage their current investments while enhancing  analytics capabilities.

Use Scenarios

Our Data Lake Solution is a robust tool designed for storing and analyzing vast amounts of data. DataCanva provides a range of services to help you build an effective data lake tailored to your business needs. Leveraging the versatile capabilities of the Data Lake Solution, built on the solid foundation of DataCanva, you can create a data lake that aligns perfectly with your organizational requirements.

Our Data Lake Solution support a wide range of scenarios, including:

Machine Learning and AI

Large datasets stored in data lakes can be utilized to build and train machine learning models, facilitating predictive analytics and AI-driven decision-making.

Real-time Analytics

Data lakes enable the processing and analysis of streaming data in real-time, allowing organizations to gain timely insights and respond instantly to events or anomalies.

IoT Data Processing

Businesses can ingest and analyze vast amounts of data from IoT devices, helping to detect patterns, identify anomalies, and optimize device performance.

Clickstream Analysis

By analyzing user behavior data from website logs, businesses can gain insights into user engagement, click patterns, and conversion rates.

Business Intelligence and Analytics

Data lakes provide a platform for conducting business intelligence and analytics on large datasets, empowering organizations to make informed decisions, uncover new opportunities, and enhance their bottom line.

Regulatory Compliance

Data lakes can store and manage information required for regulatory compliance, assisting businesses in meeting various regulations, such as the General Data Protection Regulation (GDPR).

Data Exploration

They also offer self-service analytics capabilities, enabling business users to explore, visualize, and report on data independently.