Revamping Data Management Strategies with Data Pipelines

Table of Contents

Introduction

Modern businesses are constantly adapting in terms of their data management strategies for competitive advantage. With the amount of new data coming in, managing data without affecting the overall data flow is a complex task. This is where data pipelines come into the picture.

Data pipelines bring all data silos into one place and create a highly scalable and adaptable data management system. They bring digital transformation to your data-driven business needs.

This blog will walk you through what are data pipelines and data management strategies. We'll see how data pipelines can help you revamp your data management strategy and go over some important considerations while building your data architecture.

What Are Data Pipelines?

A data pipeline corresponds to a series of data processing steps to facilitate the movement of data from one place to another. Data pipelines control the overall process workflow and the data ingestion in and out of the data storage and data warehouse.

Data pipelines work just like a normal pipeline that carries something from a source to a destination. Usually, data pipelines ingest raw data, unless it is data coming from a Software as a Service (SaaS) application. The destination can be a data warehouse, a data lake, or a data analytics or business intelligence application. The details of the destination in a data pipeline depend on the business use case.

Data pipelines also consist of some key components like data transformation operations, data ingestion models, workflow management, and monitoring system. All these make a complete data pipeline.

What Is a Data Management Strategy?

A data management strategy is an organization’s plan to effectively use big data to achieve business goals. It covers all the metrics related to managing data and getting to the desired business goals. It usually focuses on the data storage plans, the data transformation methodology, the end-user knowledge, the size and type of data, and what is needed to achieve the end goals.

A data management strategy provides the groundwork for establishing data governance, automation, and analytics processes. It basically controls all the strategies involved in the big data lifecycle and aims to achieve the best data model.

How Do Data Pipeline Differ from Traditional Data Management Strategies

In the past, data management strategies were mostly based around just data storage, ingestion, transformations, and access. There wasn't as much focus on scalability, and neither was dealing with volume a daily basis problem.

Now, with the advancements in data engineering capabilities and machine learning technologies, strategies for real-time data access are required. Businesses now have to scale quite more often to adjust to the big data demands. Traditional data management strategies only provide short-term solutions.

Data pipelines offer highly scalable solutions to big data and real-time data management problems. They bring automation to the table and can integrate modern machine learning and data science techniques into the system.

Data pipelines are essential for many modern uses, like real-time streaming data processing, moving data between on-premises and cloud systems, and data ETL for ML.

Choosing a Good Data Management Strategy

Choosing a good data management strategy is vital for the big-data-reliant business's success. Here are some important practices to achieve this:

Clarify the Business Objective:

The first and the most important thing for a good data management strategy is to lock your business objective. Stakeholders and business intelligence (BI) strategists must have clarity on their business goal, the target market details, their data requirements, the technologies required, data storage requirements, type of data ingestion, and destination. If this is not achieved, the stakeholders may end up overspending on data or technology that is not even needed.
Choosing the Most Compatible Data Engineering, Transformation, and Visualization Tools for Your Business:

It is very important to explore and take the initiative on which data engineering tools should fit your business needs. It could be a Snowflake technology or a self-service tool. It should be compatible with the business requirements.
Ensuring Data Governance:

Data governance is the process of establishing data policies for the complete data flow, transformations, and usage. It helps in metadata management, data quality, and security for self-service data access across the entire enterprise data. A good data management strategy must have a data governance framework implemented to ensure the quality and security of data.
Data Quality:

BI strategists should also select the best policies and tools for ensuring good data quality and managing faulty data sets.

How Do Data Pipelines Fit in the Data Management Strategy?

Data pipelines fit into the data management strategy in several ways:

They provide a means of automating and streamlining data movement and processing. This is useful for stakeholders, as it helps them save time and effort. The tools also make data more accessible and useful for decision-making and strategic planning.
Data pipelines can help improve the quality and accuracy of raw data. They do this by allowing organizations to apply consistent metrics and transformations to the data as it moves from one location to another. As a result, the data is more reliable and trustworthy.
Data pipelines can help organizations integrate data from multiple sources and formats. This provides better observability of the data. Organizations can gain valuable insights and make more informed decisions based on a wider range of data.
Self-service data pipelines, created using tools and platforms, allow users to connect easily to data sources, create queries and analyses, and generate reports and visualizations. Need for programming or other technical expertise is not necessarily required.
Short-term data pipelines process and analyze data for a specific period rather than on an ongoing basis. These pipelines often support specific initiatives or events, such as seasonal sales or marketing campaigns.

Considerations to Build the Right Data Pipelines – Data Pipeline Use Cases

While building data pipelines, it is significant to consider the following:

Data sources and destinations
Data transformation and enrichment processes
Performance and scalability requirements
Security and compliance requirements
Data storage requirements

We'll now see how these considerations are relevant via some data pipeline-building use cases.

Data Streaming Service

Data pipelines facilitate the data management system greatly when the app or system has a data streaming service. For building a data pipeline for a data streaming service, some of the key considerations are:

Data ingestion should be handled in real time for large data.
Any data storage or data flow problem and the service may stop and affect the product outlook.
The data pipeline should be secure with an in-place data governance framework.
The data pipeline should be flexible and scalable and should be able to adapt if there is any change in requirements.
The data pipeline should be optimized for performance.

Data Warehousing and Analytics

Data warehousing provides data integration into one place to facilitate analysis. While building a data pipeline for data warehousing and analytics, it is important to consider:

What ingestion sources does the data warehousing require?
Which data ETL tools does the use case require?
What data security methods are needed to protect the data set once loaded?

Once the data is stored in the data warehouse, data scientists and other users can access and analyze the data using a range of tools and techniques.

This could involve running ad hoc queries, building data models, or applying data science techniques to generate insights and support predictive analytics and other data analytics use cases.

Organizations can improve workflows and decision-making by centralizing and organizing enterprise data in a data warehouse.

This example shows how a data pipeline can join together large amounts of data from multiple sources into a single analytics platform on Azure. Primarily designed for a sales and marketing solution, its design principles are transferable to many industries that require the analysis of big datasets, such as e-commerce, retail, and healthcare.

Data Mesh

Data mesh is a decentralized data architecture that organizes data sources by their specific business domains. When building a data pipeline for a data mesh architecture, the most important things are:

Ensuring data security and data governance
Support for a wide range of data sources
Making sure the pipeline complies with the data mesh architecture principles
Scalability to get to optimal performance and cost

Read more about Data Mesh: Is Data Mesh the Right Framework for Your Data Ecosystem?

Build Smart Data Pipelines with Integrate.io

Data pipelines can improve data management strategies by enabling quick and easy data flow, transformation, and analysis. They improve data quality, increase efficiency and collaboration, and offer a faster timeline for data ingestion.

Integrate.io provides no-code solutions to your data pipeline headaches. Using their state-of-the-art ETL and data management services, you can create a highly scalable and efficient data pipeline for your business. The systems can provide about 200 transformations without affecting other workloads.

Schedule a call with Integrate.io to learn more about their services.

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