Building a path from ingestion to analytics
Last updated
Last updated
BDB’s Data Pipeline which can read Data from Real-time, Near Real-time, Batch(scheduled) Process from IOT, Websites, Third Party APIs, Data Feeds. BDB Platform can handle the massive amount of data & cleansing through Data Quality procedures. Our experienced Data Science team has specialized algorithms to offer accurate Customer Analytics. BDB Decision platform owns off the shelf AI-based Algorithms for Text, Voice, Sentiments, Image, and Video Analytics. BDB provides high-end visualization module with drag and drop interface to build governed dashboards and self service dashboard with AI based Search. BDB Data pipeline is an event based serverless architecture, deployed on the Kubernetes cluster and Kafka based communication to handle real-time and batch data. Gain seamless insights into a massive amount of structured, semi-structured, and unstructured data. Support your decisions with advanced Machine Learning Algorithms and Visualization techniques, all in one go.
BDB Data pipelines are used to manage data through each step in this lifecycle, from ingestion to utilization. Data pipelines are incredibly important, especially for data science. Models are only as good as the data they consume, and data pipelines ensure data is up to date, cleansed, and ready for use.
Data pipeline architecture is the design and structure of code and systems that copy, cleanse or transform as needed, and route source data to destination systems such as data warehouses and data lakes.
Three factors contribute to the speed with which data moves through a data pipeline:
Rate, or throughput, is how much data a pipeline can process within a set amount of time.
Data pipeline reliability requires individual systems within a data pipeline to be fault-tolerant. A reliable data pipeline with built-in auditing, logging, and validation mechanisms helps ensure data quality.
Latency is the time needed for a single unit of data to travel through the pipeline. Latency relates more to response time than to volume or throughput. Low latency can be expensive to maintain in terms of both price and processing resources, and an enterprise should strike a balance to maximize the value it gets from analytics.
Data engineers should seek to optimize these aspects of the pipeline to suit the organization’s needs. An enterprise must consider business objectives, cost, and the type and availability of computational resources when designing its pipeline.
let's review the fundamental components and stages of data pipelines, as well as the technologies available for replicating data.
A data pipeline architecture is mainly applied to improve the targeted functionality of data in business intelligence or analytics software. Business organizations can gain meaningful data insights into real-time trends and information as data pipelines deliver data in chunks, through suitable formats, designed for specified organizational needs.
Runs on Kubernetes container and Provides easy scalability and fault tolerance
1. Build using Kafka for event management and streaming, and Apache Spark for distributed data processing
2. Supports batch wise and streaming (real-time) computation processes
3. Seamless integration with Data preparation and Predictive Workbench.
4. Ability to run custom scripts (E.g., Python, SSH, and Perl)
5. Logging and monitoring facilities
6. Drag and drop panel to configure and build desired Data Pipeline workflows
7. Create Custom Components as per your business requirement
8. ML Workbench Support
9. Real-time Analytics of IoT
Data pipeline architecture is layered. Each subsystem feeds into the next, until data reaches its destination.
The origin component is where the data enters the pipeline. This is the original source of the data: anything from a text file to a database table. It may also be a continuous stream of data. The purpose of the origin is to obtain data from the source and, in some cases, a secondary aim is often to obtain any new or updated data generated to continuously refresh the data at the destination. This is usually achieved by establishing criteria that, when detected, will trigger pipeline updates — such as a scheduled execution time or detected changes to source systems.
The ingestion components of a data pipeline are the processes that read data from data sources — the pumps and aqueducts in our plumbing analogy. An extraction process reads from each data source using application programming interfaces (API) provided by the data source. Before you can write code that calls the APIs, though, you have to figure out what data you want to extract through a process called data profiling — examining data for its characteristics and structure and evaluating how well it fits a business purpose.
Once data is extracted from source systems, its structure or format may need to be adjusted. Processes that transform data are the desalination stations, treatment plants, and personal water filters of the data pipeline. The processing layer is in charge of transforming data into a consumable state through data validation, clean-up, normalization, transformation, and enrichment.
Transformations include mapping coded values to more descriptive ones, filtering, and aggregation. Combination is a particularly important type of transformation. It includes database joins, where relationships encoded in relational data models can be leveraged to bring related multiple tables, columns, and records together.
Destinations are the water towers and holding tanks of the data pipeline. A data warehouse is the main destination for data replicated through the pipeline. These specialized databases contain all of an enterprise's cleaned, mastered data in a centralized location for use in analytics, reporting, and business intelligence by analysts and executives.
Less-structured data can flow into data lakes, where data analysts and data scientists can access the large quantities of rich and minable information.
Finally, an enterprise may feed data into an analytics tool or service that directly accepts data feeds.
The final component of a data pipeline is monitoring. Monitoring is a key part of any core process, especially when dependencies are involved. The user can monitor a pipeline together with all the components associated with the same by using the ‘Pipeline Monitoring’ icon. The user gets information about Pipeline components, Status, Types, Last Activated (Date and Time), Last Deactivated (Date and Time), Total Allocated and Consumed CPU %, Total allocated and consumed memory, Number of Records, and Component logs all displayed on the same page.
Monitoring ensures that the data pipelines stay healthy and that you can actively rectify any issues before causing bigger problems for downstream data consumers.
Data may be continuous or asynchronous, real-time, or batched or both. Data may be ranging from UI activities, logs, performance events, sensor data, emails, social media to organizational documents, our Lambda architecture saves users from the nitty-gritty of data interaction and facilitates smooth data ingestion. BDB Data Pipeline supports basic and advanced level data transformations through in-built components and integrated Data Preparation scripts to enhance data insight discovery.
Data Pipeline stores each ingested data element with a unique identifier tagged with a set of extended metadata tags which can be queried for further data analysis. It has a combination of cloud-based, on-premise, and hybrid software applications such as S3, HDFS, ES, JDBC and Cassandra data writers to store the processed data or pass it on for interactive visualization.
BDB Data Pipeline is a web service that can be used to automate the end-to-end movement and transformation of data. Create data-driven workflows to get appealing insights and visualization for better decision making.
Embed any ML or analytics models from the BDB Predictive Workbench implanting advanced analytics on the collated data.
Consume Data Preparation scripts from the BDB Data Preparation modules for faster data processing and cleaning.
Write the final output of data in data service or data store to visualize the processed data through governed dashboards (BDB Dashboard Designer) or interactive self-service BI reports (BDB Business Story).
Use Case:
Hyper automation and analytics use case for online Retail
Webinar 3 - Hyper automation & Analytics use case for Online Retail
