💯Core Concepts
A full-stack data platform is a comprehensive and integrated system designed to handle all aspects of data management, from data collection and cleaning to modelling and visualization.
It combines multiple layers of data categories to provide end-to-end capabilities to build and manage an organization’s data infrastructure throughout its lifecycle. A full-stack data platform acts as a centralized hub where data teams can perform core data operations.
Data Warehouse
A data warehouse acts as a centralized repository for structured and unstructured data by integrating information from siloed sources. It is optimized for analytical queries, thereby providing a foundation for reporting, analysis, and decision-making processes.
Understanding how to establish your data warehouse is integral to building a scalable and efficient data platform. A strong data warehouse provides a unified view to analyze the organization's data instead of scattered sources. It stores data long-term, enabling users to dig into historical information for forecasting, spotting trends, and learning from past business impacts. A cloud data warehouse sits at the centre of every data stack, in other words, you can’t build a full stack without a data warehouse.
To learn more about data warehousing, refer to Data Warehouse.
Data Ingestion
Data ingestion is the next step after setting up a data warehouse. Data ingestion helps import raw data from various sources into a centralized storage or processing system, such as a data lake or data warehouse. The data can have minimal transformation during data ingestion to ensure it conforms to the target data format or schema, but the primary focus is on moving the data efficiently to build a data pipeline.
A full-stack data platform may facilitate automated batch and real-time data pipelines to ensure timely access to the latest information. Data Ingestion vendors provide fully managed connectors to automate and centralize all data into the destination, without worrying about API changes from the data sources.
To learn more about data ingestion, refer to Data Replication.
Data Transformation and Modeling
Data transformation is the pivotal next step in refining raw data into a clean, structured format for further data analysis. Data transformation is implemented at a semantic layer where data teams can centrally define and store key performance indicators in code. Therefore, it helps establish a singular source of truth for data management across the organization ensuring data integrity and consistency. Data transformation tools provide a unified platform to clean and organize data using version control management and structured query logic.
Data modeling is the process of designing the structure of a database or data warehouse to facilitate efficient storage, retrieval, and analysis of data. It acts as a blueprint for a database or data warehouse by arranging data in a logical structure and fine-tuning database performance for optimal efficiency. One of the key features of a full-stack data platform is setting up data modelling for the infrastructure to view the data lineage of each metric, how it’s built, what the data source is, and how it’s consumed. The process empowers seamless data exploration and informed decision-making.
To learn more about data ingestion, refer to Metrics Layer.
Data visualization and analysis
Data Visualization leverages software and services to transform data into actionable insights that inform an organization's strategic and tactical business decisions. Once data transformation and modeling are completed, data teams perform data analysis and visualization to extract insights and drive informed decision-making. Data analysis and visualization are performed using Business Intelligence (BI) tools where users can analyse data sets and present analytical findings via reports, summaries, dashboards, graphs, charts, and maps to provide users with detailed intelligence about the state of the business.
Users can integrate visualization tools into the data platform, to directly create and customize visualizations from the data source. Interactive visualization features are often implemented to derive specific data points, filter data based on criteria, and explore trends dynamically. Data analysis involves examining datasets to discover patterns, trends, correlations, and insights that can inform business decisions. Users can integrate data analysis tools directly into the data platform for automated analysis pipelines to enable proactive decision-making.
To learn more about data ingestion, refer to Business Intelligence.
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