Build a successful data architecture using data engineering

27 March 2023

Data architecture manages the lifecycle of data. Data architecture weaves a coherent and comprehensive blueprint that ensures seamless data flow within storage systems from data collection to distribution and consumption. This helps organizations to optimize their data processing operations. Artificial intelligence (AI) applications leverage diverse information to provide innovative solutions that are firmly grounded in data architecture principles.

While data architecture outlines every step in the data management process, data engineering prepares the data for use by data analysts, BI developers, and other data users. It requires dedicated experts—data engineers—to design and build systems to gather and store data at scale and prepare it for further analysis.

Data engineering step-by-step process

As businesses grow and evolve, they often grapple with disparate data management systems such as ERP, CRM, and other mission-critical applications, along with real-time information from IoT devices and unstructured data files. This data fragmentation hinders the organization’s ability to understand its current business state or to run comprehensive analytics. Data engineering addresses this challenge by integrating, cleaning, and transforming these varied data sources into easily accessible and consumable formats.

Data engineering structures an organization’s data landscape. It unlocks its analytics capabilities’ full potential and makes more accurate data-driven decisions. The data engineering process involves a series of tasks that transform a large amount of raw data into information assets for use by data analysts and data users. Typically, the workflow consists of the following stages:

Data ingestion: Moves structured and unstructured data from multiple sources such as SQL and NoSQL databases, IoT devices, websites, and streaming services to a target system where it will be transformed for analysis.

Data transformation: Fine-tunes disparate data to meet end users’ needs. It removes errors and duplicates from data, normalizes it, and converts it into the required structure.

Data serving: Delivers transformed data to users such as analysts, business stakeholders, or data science teams through a BI platform or dashboard.

Data engineering stages

The need for a data pipeline

The data pipeline automates the data engineering process of ingestion, transformation, and serving steps. It combines tools and operations to move data between systems for storage, analysis, and further handling. Data engineers construct and maintain data pipelines for the following activities:

  • Data migration from on-premises to cloud databases.
  • Convert raw data into a usable format for BI, analytics, and machine learning projects.
  • Integrate data from disparate systems and IoT devices.

Configuring secure and reliable data flow is challenging as many things can go wrong during data movement: 

  • Data may be corrupted.
  • Data may hit bottlenecks, causing latency.
  • Data sources can conflict, generating duplicate or incorrect data. 

Unifying data requires careful planning and testing to eliminate duplicates and incompatible data types and to secure sensitive data. 

Two significant pitfalls in building data pipelines are:

  • Lack of suitable metrics
  • Miscalculated data load

ETL and ELT pipeline

ETL (Extract, Transform, Load) pipeline is the most common architecture that automates the following processes:

Extract: retrieve raw data from numerous sources—databases, APIs, and files.

Transform: standardize data to meet the format requirements and improve data discoverability and usability.

Load: save data to a new destination in a usable format, typically a database management system (DBMS) or data warehouse.

After importing data into a centralized repository, it can be used for analysis and business intelligence tasks, such as pattern detection, trend discovery, reporting, and visualization.

The ELT pipeline performs the steps in a different order: Extract, Load, and Transform. Here, you place all the extracted data in a data warehouse, data lake, or lakehouse, which are later processed, fully or partially. Large-scale projects often combine these data pipelines to enable traditional and real-time analytics.

Types of data architectures

1. Data warehouse

A cloud data warehouse is a central repository for businesses to manage and process large volumes of data. It can optimize read, aggregate, and query functions, improving operational reporting and analysis. In addition, data warehouses have made it more convenient for data scientists to extract relevant data without jeopardizing operational efficiency or causing discrepancies in findings.

The modern data warehouse can handle structured and unstructured data formats such as images, PDFs, and audio files. As a result, data warehouses serve as the crucial bridge between raw information and informed decision-making. Without them, inconsistencies, delays, and outages might plague the reporting process, hindering the organization’s operations. As a result, data warehouses play a pivotal role. They ensure streamlined reporting and analysis, enabling businesses to confidently make data-driven decisions and accurately forecast key metrics essential for their growth and success.

2. Data lakes

A data lake can store a vast pool of raw, unstructured data. It is a highly agile storage system that holds data in its native, unprocessed form. Unlike traditional data warehouses with fixed configurations, data lakes thrive on unstructured data, enabling data loading immediately after extraction. As your project grows and requires a more sophisticated architecture, a data lake becomes an invaluable architecture. Its versatile nature allows you to load data, store it, and adapt it as necessary, even when the intended purpose of the data remains unknown.

Data lakes serve as a treasure trove for data scientists and machine learning engineers, allowing them to access raw information and transform it into a valuable asset for predictive analytics and advanced machine learning models.

3. Data lakehouse

The lakehouse presents an innovative solution in data management by integrating the advantages of data lakes and data warehouses. With the advent of low-cost cloud storage and highly reliable systems, a lakehouse’s open architecture allows the direct implementation of data structures and management features that were once limited to data warehouses.

As our digital world expands, enterprise-grade systems must prioritize security measures and access control. In addition, integrating data governance capabilities such as auditing, retention, and lineage has become indispensable, especially with the increase in privacy regulations. Complementing these efforts, tools enabling data discovery, such as data catalogs and usage metrics, are increasingly integral for organizations to navigate and manage their digital resources. 

In Summary

  • A data warehouse deals with structured data for self-service BI and analytics.
  • A data lake handles large volumes of structured and unstructured data to support machine learning and AI.
  • A data lakehouse supports structured and unstructured data, which allows portability and efficient processing.

Modern data architecture using data engineering 

As you build your roadmap to accommodate tomorrow’s applications—including AI, blockchain, and Internet of Things (IoT) workloads—you need a modern data architecture to support the data requirements.

Following are the top seven characteristics of a modern data architecture built with current data engineering practices:

  • Cloud-native and cloud-enabled to leverage the cloud’s elastic scaling and high availability.
  • Robust and scalable data pipelines which combine cognitive analytics, intelligent workflows, and real-time integration in a single framework.
  • Connect to legacy applications using standard API interfaces.
  • Enable real-time data. Validate, classify, manage, and govern data.
  • Decoupled and extensible framework to eliminate dependencies between services.
  • An open standard format to enable interoperability.
  • Cost-optimized and simple.

How PreludeSys builds a successful data architecture using data engineering

PreludeSys applies data engineering principles to rapidly access and process the organization’s vast data volumes. We prepare data stored in different systems and formats by consolidating them to a central repository such as a data warehouse or a lakehouse. We then implement (design and architect) the most efficient and reliable data pipeline to make data usable for further analysis.

Outcomes of our data engineering solution:

  • Scalable, low-cost data architecture.
  • Reliable and resilient framework to process and access data.
  • Improved quality, speed, and accuracy.
  • Reduce data processing time from days to hours.

With over 150 Microsoft-certified experts in-house and 50 successful project deliveries, we consistently demonstrate that companies who bring us their complex data challenges quickly become satisfied long-term customers.

Talk to an expert to learn more about our Data Engineering Services!

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