With MudahLiving™ BigData® as a solution for managing, processing and displaying big data in the form of analytics, geospatial presentation, you can quickly process large data sets with varied data to get a more comprehensive picture of statistics, processes, locations, predictions and much more.

Mudah Living Platform

Mudah Living is an End-to-End Data Management and Analytics Platform designed to help organizations manage and analyze large amounts of data across multiple environments and data formats, to provide insights, problem solving, or create value for businesses or organizations.

Horizontal Services

End to End Big Data Platform to generate insights that suit the company’s operational and business needs.

Vertical Services

Helping companies to transform complex data into actionable insights with various use cases across Industries.

Consultation

Helping companies build data-driven organizations with expert support, training & consulting services.

Mudah Living Principle

Big Data requires reliable technological support, especially in handling large data volumes, complex varieties and fast velocity.

1. Acquired Layer related to obtaining data, both structured and unstructured data.

2. Accessed Layer related to data access power, the data that has been collected requires governance, integration, storage and computing so that it can be managed for the next stage.

3. Analytic Layer related to the insights to be obtained, the results of managing the data that has been processed.

4. Application Layer is the final stage, where the results of the analytics are visualized and reported to support decision making.

Mudah Living Architecture

Mudah Living Framework

Mudah Living Feature

Mudah Data Store

Streaming Processing

Mudah Data Store handles streaming data processing using a topic-based and partition-based system that enables high scalability and parallel processing. Data is sent by producers to specific topics and retrieved by consumers for processing. MLDA runs in a cluster consisting of multiple brokers to ensure high availability and redundancy. The Mudah Data Store Streams API and KSQL enable real-time data processing directly within Mudah Data Store, leveraging log-based storage for efficiency and data replay capabilities.

Batch Processing

Each DAG comprises a series of tasks defined as nodes, which represent discrete steps in the data ingestion process. These tasks can include data extraction, transformation, and loading (ETL) operations. Mudah Data Store schedules and monitors these tasks, ensuring they run in a specified order, handle dependencies, and manage retries upon failure. With its rich scheduling capabilities, MLDA can trigger batch jobs at regular intervals or based on specific events. It also provides detailed logging and alerting mechanisms to track job statuses and troubleshoot issues efficiently.

Mudah Data Lake

In the Mudah Data Lake, Hadoop and Spark handle data access and storage through a distributed file system and advanced processing engines. Hadoop uses the Hadoop Distributed File System (HDFS) to store large datasets across a cluster of commodity hardware, ensuring fault tolerance and high availability. Data is broken into blocks and replicated across multiple nodes.

Hadoop’s MapReduce framework processes data in parallel across these nodes. Apache Spark, on the other hand, can also utilize HDFS for storage but leverages its in-memory computing capabilities for faster data processing. Spark processes data using Resilient Distributed Datasets (RDDs), which can be cached in memory to accelerate iterative algorithms and interactive data analytics.

Both frameworks enable efficient data handling in large-scale environments, but Spark’s advanced memory management offers significant performance improvements for specific workloads.

Mudah Analytics

Mudah Analytics is a real-time data processing system designed for interactive analytics on large datasets, enabling low-latency data retrieval and high performance, with powerful relational database management system (RDBMS) with extensive features for analyzing both structured data and GIS (Geographic Information Systems) data.

Also in have an interactive notebook framework used for creating queries and data analysis scripts. By integrating capability for real-time analytics, relational data management and analysis, and Interactive notebook for querying and visualization, we can build a comprehensive and efficient analytics ecosystem that supports various data analysis needs from retrieval to presentation.

Mudah Visualization

Mudah Visualization is a powerful data visualization tool designed to explore and visualize complex datasets. It allows users to create interactive dashboards and charts without requiring extensive programming knowledge. Mudah Visualization connects to a wide variety of data sources, including SQL databases, and provides a rich set of visualization options such as bar charts, line charts, heat maps, and geographic maps.

With its intuitive drag-and-drop interface, users can easily construct visualizations and filter data in real-time, making it an excellent choice for creating dynamic and informative data presentations. Superset also supports SQL Lab, where users can write and run SQL queries, further enhancing its capabilities for data analysis and exploration.