Processing Modern Data Pipeline
Last updated
Last updated
According to a McKinsey Global Institute study, data-driven businesses are 23 times more likely to acquire new customers, nine times more likely to keep customers, and 19 times more likely to be profitable. However, data volumes (and sources) are on the rise. As a result, organizations must seek the most efficient way to collect, transform, and extract value from data to stay competitive.
A Data Pipeline is a sequence of components that automates the collection, organization, movement, transformation, and processing of data from a source to a destination to ensure data arrives in a state that businesses can utilize to enable a data-driven culture.
Data pipelines are the backbones of data architecture in an organization. Implementing a well-designed, robust, and scalable data pipeline in your organization can help your organization effectively manage, analyse, and organize the copious amount of data to deliver business value.
While all this data holds tremendous potential, it is often difficult to mobilize for specific purposes. Until recently, businesses had to be selective about which data they collected and stored. Compute and storage resources were expensive and sometimes difficult to scale. Today, the rise of affordable and elastic cloud services has enabled new data management options— and necessitated new requirements for building data pipelines to capture all this data and put it to work. You can accumulate years of historical data and gradually uncover patterns and insights. You can stream data continuously to power up-to-the-minute analytics.
This white paper describes the technical challenges that arise when building modern data pipelines and explains how these challenges can be solved with the BDB Platform by automating performance with near-zero maintenance. The BDB platform offers native support for multiple data types and can accommodate a wide range of data engineering workloads to build continuous data pipelines, support data transformation for different data workers, operationalize machine learning, share curated data sets, and other tasks. BDB customers benefit from a powerful data processing engine that is architecturally decoupled from the storage layer, yet deeply integrated with it for optimized performance and pipeline execution.
Data pipelines automate the transfer of data from place to place and transform that data into specific formats for certain types of analysis.
They make this possible by performing a few basic tasks:
Capturing data in its raw or native form
Ingesting data into a data warehouse, data lake, or other types of data store, housed on-premises or in the cloud
Transforming data into a business-ready format that is accessible to users and applications
Augmenting data to make it more valuable for the organization
In a modern data pipeline, data moves through several stages as it is transformed from a raw state to a modeled state. To augment the value of data, data must be deduplicated, standardized, mapped, integrated, and cleansed to prepare it for immediate use.
Today’s modern data pipelines have arisen in response to three important paradigm shifts in the software industry.
These pipelines depended on extract, transform, and load (ETL) workflows, where data was processed and transformed outside of the “target” or destination system. These traditional ETL operations used a separate processing engine, which involved unnecessary data movement and tended to be slow. Furthermore, these engines weren’t designed to accommodate schemeless, semi-structured formats—a death blow in today’s world of continuous and streaming data sources. To accommodate these newer forms of data and enable more timely analytics, modern data integration workloads leverage the processing power of target data platforms in the cloud.
With traditional ETL architectures on-premises, ETL jobs contend for resources with other workloads running on the same infrastructure. In contrast, modern ELT architectures move these transformation workloads to the cloud, enabling superior scalability and elasticity.
Many businesses produce data continuously, but they may only make updates available for analytics at periodic intervals, such as weekly, daily, or hourly, typically via bulk or batch data-update processes. This ensures good compression and optimal file sizes, but it also introduces latency—the time between when data is born and when it is available for analysis. Latency delays time to insight, leading to lost value and missed opportunities. `
A more natural way to ingest new data into your technology stack is to pull it in continuously, as data is born. Continuous ingestion requires a streaming data pipeline that makes new data available almost immediately, such as a few seconds or minutes after the data is generated.
Database schemas are constantly evolving. Modifications at the application level, such as the introduction of semi-structured JSON data to augment structured relational data, necessitate changes to the underlying database schema. Application developers may need to constantly interface with database administrators to accommodate these changes, which delays the release of new features. To overcome these limitations, modern data platforms include built-in pipelines that can seamlessly ingest and consolidate all types of data so it can be housed in one centralized repository without altering the schema. However, legacy systems might stand in the way of this goal. Data warehouses typically ingest and store structured data, defined by a relational database schema. Data lakes store many types of data in raw and native forms, including semi-structured data and some types of unstructured data. It may be challenging to integrate end-to-end data processing across these different systems and services.
As organizations attempt to take advantage of these paradigm shifts, fulfilling their expectations has become progressively more difficult. Common obstacles include the following:
Data of every shape and size is being generated, and it often ends up sequestered in siloed databases, data lakes, and data marts—both in the cloud and on-premises. Different applications have different expectations for the delivery of data, forcing data engineers to master new types of languages and tools.
Organizations with legacy data pipeline architectures operate with a fixed set of hardware and software resources, which invariably leads to reliability and performance issues. As data pipeline workloads increase, system administrators may need to juggle the priorities of other workloads contending for those same resources.
Complex data integration and orchestration tools may be necessary to create new data pipelines and hand-coded interfaces. Many IT teams find that they spend too much time managing these pipelines and the associated infrastructure. Rather than focusing on advising the business on how to get the most value out of organizational data, these highly paid technology professionals are mired in technical issues related to capacity planning, performance tuning, and concurrency handling.
A BDB Platform 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.
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 Pipeline is a platform that can be used to automate the End-to-End movement and transformation of data. With BDB Data Pipeline, you can define data-driven workflows to get appealing insights and visualization for better decision-making.
Data may be continuous or asynchronous, streaming in real-time or batched, or both. Data may be ranging from UI activities, logs, performance events, sensor data, emails, and 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-premises, 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.
The Export icon (as highlighted in the above-given image) appears for a cleansed data on the Data Preparation header panel to export the current Preparation to the BDB Pipeline module.
In the BDB Data Preparation plugin after the data gets cleansed an option appears to export into the BDB Data Pipeline (which is highlighted in the above image). This option makes the BDB Data Pipeline more powerful to get valuable insights.
Write the final output of data in a 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).
BDB is a Low-Code, Hyper Automation, Data Analytics (AI/ML) platform that accelerates (often 3x-5x faster vs Competition) Data Ops & AI Ops for Enterprises on course to Digitization & Data Monetization.
Consider going through the Hyper automation & Analytics use case for Online Retail using the below given video:
