Published March 22, 2023
Data Platform Layers
From storage and processing to visualisation and application layers, learn how each layer contributes to creating a robust and efficient data platform. Understanding the distinct layers of a data platform is vital for deriving insights, optimising operations, and supporting business decisions,
Data ingestion layer
The data ingestion layer is a vital component in any data platform, responsible for collecting, importing, and preparing data. This responsibility involves multiple subcomponents, such as data extraction, data transformation, and data loading, which work together to streamline the entire process. The process of acquiring, preparing, and storing data sets the stage for subsequent processing, analysis, and visualisation tasks. These tasks depend on an efficient and optimised data ingestion layer, which can improve the quality, scalability, and overall performance of the data platform.
Data extraction is the initial step in obtaining data from data sources. These sources range from databases and files to APIs and web services, each contributing a unique type of data to the pipeline. The pipeline requires both structured and unstructured data to obtain a complete image. This data flow is required for enabling the raw data to be available for subsequent processing, paving the way for the next stage in the data ingestion layer.
Data transformation is responsible for converting raw data into a format suitable for the data storage layer. This process may involve cleaning, filtering, aggregation, and format conversion. Data transformation’s primary goal is to improve the quality and usability of the data, which is essential for the subsequent stages of the data platform. The transformed data then moves to the final subcomponent, data loading.
Data loading is the final step in the data ingestion layer, focusing on transferring the transformed data into the data storage layer for subsequent retrieval, processing, and analysis. This process involves two primary methods: batch loading and real-time (or streaming) loading, each with its unique advantages and use cases.
Batch loading is a technique that processes and transfers large volumes of data at predetermined intervals or scheduled times. This approach is suitable for scenarios where data can be processed and stored in bulk, without the need for immediate analysis. Batch loading is often more resource-efficient, as it consolidates data processing tasks, which can lead to cost savings and reduced system strain.
In contrast, real-time loading continually processes and ingests data as it becomes available, offering near-instantaneous access to the latest information. Real-time loading is ideal for situations where timely data analysis is critical, such as monitoring live events, tracking user behaviour, or detecting anomalies in near real-time. However, this method may require more computing resources and infrastructure to handle the continuous flow of data.
Data storage layer
The data storage layer is a critical component in data platforms, responsible for storing and managing ingested data. This responsibility involves multiple subcomponents, such as data storage types, data indexing, and data partitioning, which collectively facilitate efficient data management and retrieval. Efficient data management and retrieval are essential for supporting subsequent data processing, analysis, and visualisation tasks.
Data Storage Types
Data storage types cater to diverse data formats and use cases within the data platform. These storage types include relational databases, NoSQL databases, data warehouses, and data lakes. Relational databases excel at handling structured data, while NoSQL databases accommodate unstructured or semi-structured data. Data warehouses serve as repositories for historical data, and data lakes store raw data for flexible processing. The choice of storage types depends on the data platform‘s requirements and the nature of the data being stored.
Data indexing, a key process in the data storage layer, organises data based on specific attributes or keys. Organising data in this manner enhances data retrieval efficiency, as it accelerates searching and filtering operations. Accelerated operations, in turn, improve the performance of data processing and analytics tasks, ensuring timely insights.
Data partitioning is a strategic technique employed in the data storage layer to distribute and manage large datasets across multiple storage units or nodes. By partitioning data, the storage layer can enhance its scalability and performance, enabling parallel processing while minimising I/O bottlenecks. There are two primary partitioning methods: horizontal partitioning, also known as sharding, and vertical partitioning.
Horizontal partitioning involves dividing data based on rows, segregating distinct sets of rows into separate storage units or nodes. This approach is effective for distributing data evenly and improving query performance when the partitioning key is chosen wisely.
Vertical partitioning entails splitting data according to columns or attributes. This method is useful for isolating frequently accessed columns from less frequently accessed ones, reducing the amount of data read during queries and improving overall query performance.
Data processing layer
The data processing layer is essential for transforming raw data into valuable insights, and it comprises various subcomponents. These subcomponents include data cleaning, data transformation, data enrichment, and data aggregation, each of which contributes to preparing data for analysis and visualisation in subsequent layers.
Data cleaning is identifying and correcting errors, inconsistencies, and inaccuracies in datasets. Correcting these issues ensures that data is of high quality and reliable for further analysis. Data cleaning tasks, such as removing duplicate records and filling in missing values, help analysts reduce the risk of drawing inaccurate conclusions from flawed data.
Data transformation is the conversion of data from its original format into a more suitable format for analysis. This conversion may include normalisation, encoding categorical variables, and applying mathematical functions. By performing data transformations, analysts can apply advanced analytics and machine learning algorithms to the data, ultimately unlocking valuable insights for business decisions.
Data enrichment focuses on augmenting existing data with additional information from external sources. This augmentation enhances the value of the data by adding context, such as geographical information or demographic details. Integrating external data, as seen in data enrichment, allows organisations to gain deeper insights and make more informed decisions based on a comprehensive understanding of their data.
Data aggregation is the final subcomponent of the data processing layer, involving the combination of data from multiple sources or records to create a summary or a new dataset. Techniques used in data aggregation include calculating averages, sums, counts, and other statistical measures. By aggregating data, organisations can identify trends, patterns, and relationships within their data, ultimately facilitating the development of effective strategies and data-driven decision-making.
Data analytics layer
The data analytics layer is the fourth critical layer in a data platform, responsible for generating insights and value from the processed data. This layer includes subcomponents such as data mining, machine learning, and statistical analysis. Each of these subcomponents plays a specific role in helping organisations make data-driven decisions and uncover patterns within their data.
Data mining is the process of identifying hidden patterns and relationships within large datasets. This technique leverages algorithms and statistical models to discover trends and correlations that may not be immediately apparent. In the context of the data platform, data mining helps organisations extract valuable insights that can inform decision-making and drive better business outcomes. For instance, data mining can be used to detect fraud, optimise marketing campaigns, or improve customer segmentation.
Machine learning, a subset of artificial intelligence (AI), enables computer systems to learn from data without being explicitly programmed. Machine learning models analyse data, identify patterns, and make predictions based on these patterns. Within the data analytics layer, machine learning can be employed to automate decision-making, enhance predictive analytics, and provide personalised recommendations. Applications of machine learning in a data platform include predictive maintenance, customer churn analysis, and sentiment analysis.
Statistical analysis involves the collection, organisation, interpretation, and presentation of data. In the data analytics layer, statistical analysis helps organisations quantify uncertainties, identify trends, and make generalisations about their data. Various statistical techniques, such as regression, hypothesis testing, and descriptive statistics, can be applied to derive insights from the data. These insights can then be used to inform decision-making, forecast future events, and identify areas for improvement.
Data visualisation layer
The data visualisation layer is a crucial part of any data platform, as it helps transform complex data into easily understandable visual representations. This layer consists of several subcomponents, including data visualisation tools, interactive dashboards, and custom reporting. Each of these subcomponents plays a vital role in helping users make informed decisions based on the data.
Data Visualisation Tools
Data visualisation tools are essential software applications that enable users to create graphical representations of data. These tools make it possible for individuals to quickly identify trends, patterns, and outliers within large datasets. Some popular data visualisation tools include Tableau, Microsoft Power BI, and D3.js. Each tool offers its unique features and capabilities, allowing users to select the one that best fits their needs and skill sets.
Interactive dashboards are a subcomponent of the data visualisation layer that provide users with real-time, visual access to their data. These dashboards often incorporate various charts, graphs, and other visual elements to present data in a visually appealing and accessible manner. Users can interact with the dashboard by filtering, sorting, and drilling down into specific data points, enabling them to gain a deeper understanding of their data. Interactive dashboards are commonly used in business intelligence and analytics applications to monitor key performance indicators (KPIs) and make data-driven decisions.
Custom reporting is another crucial aspect of the data visualisation layer, allowing users to generate reports tailored to their specific requirements. These reports can include visualisations, tables, and textual summaries that highlight essential insights derived from the data. Custom reporting enables users to focus on specific aspects of their data, target particular audiences, and meet unique business objectives.
Many data visualisation tools offer built-in custom reporting functionality, while others can be integrated with dedicated reporting software like Crystal Reports or JasperReports. By leveraging custom reporting, businesses can make informed decisions, track progress, and communicate results more effectively to stakeholders.