Given the sheer size of the data sets, a big data solution must often process data files using long-running batch tasks to filter, combine, and otherwise prepare the data for analysis. These jobs often involve reading source files, processing them, and publishing the output to new files.

If the solution has real-time sources, the architecture must include a mechanism for capturing and storing real-time messages for stream processing. This can be a basic data storage, with incoming messages being dumped into a folder for processing. Many systems require a message ingestion store to operate as a buffer for messages to allow scale-out processing, dependable delivery, and other message queuing semantics.

Change Data Capture (CDC)  is a data Integration technique that enables high-velocity data to accomplish reliable, low-latency, and scalable data replication while utilising fewer computation resources. We use CDC to provide fresh data updates in real-time to BI tools and team members, keeping them up to date. The most significant benefit of DigiXT’s CDC is that it can stream updates without directly querying the underlying source which is a significant performance boost.

Exploratory Data Analysis (EDA) assists us in identifying the underlying structure of data and its dynamics, allowing us to maximise insights. EDA is also essential for extracting key variables and detecting outliers and abnormalities. Despite the fact that there are several algorithms in Machine Learning, EDA is regarded as one of the most significant components for understanding and driving the enterprise. Through search, lineage, and striking visualisations, the DigiXT platform tells a captivating story about your data.

As there are several databases accessible to address various issues, relying on a single database to meet all programme needs might result in a non-performing, “jack of all trades, master of none” solution. The storage in DigiXT’s architecture is polyglot, which means it may be of many kinds depending on the use case. This layer supports some of the most complex persistence architectures.

This layer or module provides a contemporary data layer that allows users to access, combine, transform, and deliver datasets with unprecedented speed and efficiency. Data virtualization technology provides users with quick access to data stored throughout the enterprise, including traditional databases, big data sources, and cloud and IoT systems, in a fraction of the time and expense of physical warehousing and extract/transform/load (ETL).

This module takes your experience in management operations from reactive to proactive. DigiXT empowers you to anticipate how changes to your product, brand, or customer and employee engagement will impact the bottom line.

Most big data solutions aim to bring insights into the data via analysis and reporting. This layer allows for access-controlled data retrieval for MIS reasons. Standard access techniques, such as APIs and ODBC/JDBC, are available for downstream access, from mobile applications to web applications.

Typical big data solutions consist of repeated data processing operations included in workflows that convert source data, transfer data across many sources and sinks, as well as load, processed data into an analytical data repository or convey the findings directly to a report or dashboard. This layer is responsible for automating these workflows.

This layer ensures the success of the platform which is determined by the efficiency with which the separate components are handled, with a management console for creating users, granting access, monitoring, and implementing alerts and notifications.