In today’s scenarios, it has become a requirement for organisations to apply such an atmosphere in their data sources so that every piece of information can be utilised efficiently. When using the data for various purposes, we should always remember that the data pipeline is one of the major tools required in the process. As many say, “data is new fuel” data pipeline becomes comparable to fuel pipelines. The data pipeline is an important topic, and using this article, we will learn the following things about it.

Table of Content

• What is Data Pipeline?
• How does the data pipeline work?
• Components of Data Pipeline
• Why Data Pipelines are important?
• Modern data pipelines and ETL
• Future Improvements Needed

What is Data Pipeline?

A data pipeline can be defined as a set of tools and processes used to manage, move and transform data between two or many systems(source and destination). The motive behind applying data pipelines is to cope with every data or piece of information we get or generate. In simpler terms, we can say a data pipeline is a way that starts by collecting data from various sources and ends at a destination where data is being used to generate profits of data. Still, in between the way, multiple processes increase the usability of data at the destination source.

How does the data pipeline work?

As the above defined, it is simple to understand that the work of a data pipeline is to collect the data from different sources and take it to the destination source, where further processes like data analysis and predictive modelling work on the data. The data processing along the way depends on the requirement of use cases. However, using the data pipeline, we focus more on managing data in a more appropriate or advanced manner.

A simple example of handling data is preparing data for a machine learning program where a machine learning program is a destination, and data silos are the source. Data silos can hold all the data of an organisation and when applying machine learning for any use case organisation needs to push only a few or required data in training instead of pushing all data from data silos. In such a situation, a data pipeline takes required-raw data from silos(source) and pre-processes it along the way(null value identification, numeric transformation, dimensionality reduction etc.) and serves the data to machine learning algorithms.

Components of Data Pipeline

Using the above-given example, we can say such data pipelines will require the following components:

• Data Sources: Data sources can be of various types, such as relational databases and data from applications. Often we see that pipelines take raw data from various sources using a push mechanism, API call, and replication engine. These all together pull or supply data continuously or a webhook. Also, there may be data being synchronised in real-time or at scheduled intervals, which takes extra effort to manage data in the source.
• Data collection: This component is responsible for collecting important data into the data pipeline. Such components carry data ingestion tools so that various data sources can be connected together.
• Transformation block: The transformation of data is a must process to get completed along the way of pipelines that can include sorting, deduplication, validation, and standardisation of the data. The motive behind applying this block is to make data analysable.
• Processor: Machine learning models can ingest data in two ways. Whether in batches or in streamline. In batch processing, we collect data for predefined time intervals, and in the streamline, we process and send data to the model as data is generated.
• Workflow: This block helps in the sequencing and management of dependency. Used dependencies can be business or technical-oriented.
• Monitor: Data pipelines compulsorily have data monitoring systems so that we can get ensure data integrity. Network congestion of offline sources or destinations can be an example of a potential failure situation that can be prevented by applying alerts for such scenarios.
• Destination source: A destination source is one step before the machine learning algorithms and examples of it can be on-premises databases, cloud-based databases, or data lakes. It can also be an analytical tool like Power BI, and tableau.

The below image can be a representation of the data pipeline.

Why Data Pipelines are important?

As we all know, most organisations generate data from more than one source, and since all the data gets collected in one place, lake, or silos, it becomes complex to fetch valuable data from such complex storage. Moreover, various processes require data in time, and they really matter for an organisation’s growth, like business intelligence processes where tracking day-to-day life requires information or data every day. Avoiding mistakes in accessing and analysing data pipelines is very important.

Since organisations are requiring real-time analytics to make faster, data-driven decisions. The data pipeline eliminates the manual and time-taking steps and events from the process and enables a smooth, automated flow of data throughout the different data sources

In some places, consistent data quality is one of the major concerns and data pipelines are very helpful in ensuring the required data quality because it includes transformation and monitoring processes in it.

Modern data pipelines and ETL

Generally, ETL tools work with the data warehouses(in-house) and help in extracting, transforming and loading the data of different sources. In more recent scenarios there are various cloud data warehouse services like Google BigQuery, Amazon Redshift, Azure and Snowflake available. These warehouses are capable scale up and down the processes in seconds or minutes which helps developers in replicating raw data from disparate sources in SQL. After that, they can define transformation procedures for data and run them through the data warehouse after loading.

Also, there are various cloud-native warehouses and ETL services for the cloud are available. Using such options organisations can set up a cloud-first platform and data engineers are required to monitor ad handle failure points or unusual errors.

Future Improvements Needed

In recent scenarios, we can witness the boom in the fields of Artificial intelligence(AI), data science and machine learning. Looking at these opportunistic fields we can say that in near future every organizations that is generating the data will be using new technologies. Data and only required data is one of the major requirement to make these technology work appropriately.

In the above sections we have scene that data pipelines works and AI-ML models requires few more changes in the data like numeric data, data without null values etc. so as the demand of ML increasing changes in the data pipeline is also increasing. One major change we find is addition of feature store before the model layer. As models are data specific they works with high accuracy when required feature is provided.

Feature store can be explained as the storage system of only those daa points which are usable for the model presented in the next layer. Here we can say that feature store is one improvement and if applied in data pipeline its block diagram will look as following:

At DSW | Data Science Wizards, we have worked with many clients who were requiring AI models to be applied in their workflow and data pipeline. If we look back at our article, we can know how features stores are an essential participant in the data pipeline, especially when the pipeline is ending on an AI-model

We come with our flagship platform UnifyAI and aim to make AI available for everyone.

This platform uses some essential components to build, orchestrate and leverage AI capabilities for use cases across the domains, and the feature store is one of those components. Using Its feature store, this platform helps reduce the time of building and resolving new use cases using AI-enabled solutions.

About DSW

Data Science Wizards (DSW) is an Artificial Intelligence and Data Science start-up that primarily offers platforms, solutions, and services for making use of data as a strategy through AI and data analytics solutions and consulting services to help enterprises in data-driven decisions.

DSW’s flagship platform UnifyAI is an end-to-end AI-enabled platform for enterprise customers to build, deploy, manage, and publish their AI models. UnifyAI helps you to build your business use case by leveraging AI capabilities and improving analytics outcomes.

Connect us at contact@datasciencewizards.ai and visit us at www.datasciencewizards.ai