In today’s digital landscape, organizations that want to improve decision-making and gain a significant advantage must be data-driven, as data is a crucial resource for increasing efficiency and effectiveness. Organizations leveraging data in decision-making can gain valuable insights into their business operations, products, and industry trends. In doing so, they optimize performance, improve services, and reduce costs.
To do so, data-driven organizations often invest in advanced data analytics tools and technologies and have skilled data teams to interpret insights. However, ensuring fast response times and accommodating diverse use cases while capturing, storing, and processing datasets can be challenging for organizations handling large data volumes – one of the most significant challenges being data silos (refers to scenarios where data is not shared or integrated across different systems or departments).
Over time, the industry rose to the occasion, responding to this challenge by providing an evolving solution, data platform.
What is a Data Platform?
A data platform serves as a central repository for managing an organization’s data, allowing data-driven decisions to be made. It is a set of technologies, tools, and infrastructure that enable data collection, processing, storage, visualization, and governance.
The architecture of a data platform should be scalable and flexible to handle various data types and formats, including structured, unstructured, and semi-structured data.
A data platform is built upon several layers, each serving a specific purpose. These layers work together to provide a complete end-to-end data management solution in one place, functioning as a central location for an organization’s data (SSOT).
Platform’s Layers
Data Sources
Data sources are the various systems (external or internal to an organization) from which data is collected, such as operational databases, streaming applications, etc. Furthermore, data sources may include structured, semi-structured, or unstructured (i.e., spreadsheets, JSON, or images).
Ingestion Layer
The ingestion layer collects data from various sources into a central repository (such as a data warehouse or data lake). This layer involves the creation of data pipelines, connecting data connectors to different data sources, as well as data quality testing and data cleansing.
The data ingestion pipeline (batch-based or streaming) uses an ETL (Extract, Transform, Load) process where data is extracted with the original format, transformed, and then loaded into a destination.
A use case for ETL is an e-commerce site with sales data from multiple countries. ETL is used to extract data from each country’s database, transform it into a standardized format, and load it into a central data warehouse. The result is a comprehensive view of sales data.
Once the updated data is collected in the ingestion layer, it must merge with the existing data. However, before this merge can occur, raw data must first undergo data cleansing and quality: this includes standardization, validation, deduplication, transformation, etc.
By utilizing the ingestion layer in combination with data quality and cleansing, the collection and analysis of incomplete data is avoided. This helps prevent unnecessary waste of organizational resources.
Another similar process is ELT (Extract, Load, Transform), which can load raw data as quickly as possible before the transformation. In other words, the data is transferred in raw form without modification or filtering. When the data load is completed, it’s transformed inside the target system.
Processing Layer
The processing layer is where raw or collected data is processed into meaningful information for various purposes, such as analysis methods, ML models, applications, and more. It performs a series of operations on large amounts of data to prepare such data for user, system, and application consumption.
Techniques from machine learning, data mining, and data analytics are used to enrich data in the processing layer and prepare it for further use. These techniques include extracting insights and knowledge from large datasets. This layer provides tools for stream and batch processing.
Stream and batch processing
Stream processing refers to the ability to analyze and manipulate data created in real-time. It enables organizations to process data from multiple sources at a higher refresh rate, allowing them to analyze and act upon it quickly.
On the other hand, batch processing involves processing large volumes of data in batches with a lower refresh rate (typically done overnight, during off-peak hours, or on a regular schedule). Batch processing is often used for tasks that require significant computational resources as it can take a long time to complete (i.e., report generation).
As noted above, ELT (Extract, Load, Transform) involves extracting raw data from source systems, loading it into a target system, and transforming it as needed. The last step of ELT is transformation. This step involves processing the loaded data through data cleansing, normalization, and enrichment (among other processes) to ensure that data is prepared as required.
Storage Layer
The storage layer refers to the cloud infrastructure that manages and stores data efficiently (includes data lakes, data warehouses, etc). Depending on the use case, the layer can consist of RDBMS, S3, DHW, NoSQL databases, etc.
In addition, this layer is critical to ensure data availability, security, and low latency – with the choice of storage depending on several factors, such as cost, performance, structure, and scalability.
Serving Layer
The serving layer provides availability and quick access to data for end-users, including BI analysts, data scientists, customers, applications, and dashboards. This layer enables end-users to analyze and interact with the data in various ways. It promotes transparency and collaboration by using data visualization, which provides easy-to-understand visual information displays.
Additionally, the serving layer supports different data access patterns through a data catalog that contains metadata describing various datasets and data assets. The data catalog helps organize, classify, and document data assets in a standardized way, making it easier for end-users to find and access the necessary data.
Governance Layer
The governance layer is responsible for how an organization uses data through policies and procedures. This layer ensures data is secure and compliant with relevant industry standards. The effective governance layer involves several key processes that are essential for guaranteeing the accessibility and integrity of an organization’s data. These processes include data catalog, data observability, and orchestration.
Data catalog, data observability, and orchestration
A data catalog creates an inventory of an organization’s data assets, including information about their location, format, and metadata, making it easier for organizations to find and access their information.
Data observability is understanding, monitoring, diagnosing, and managing data health across the platform’s lifecycle. This includes an essential process that involves tracking data from its source (where it was initially created) to its changes until it’s consumed, providing a complete picture of how data moves through an organization’s systems. This helps identify situations before they have a significant impact on the business.
Finally, there is orchestration, the essential part of this layer. It refers to the coordination of tasks and enables the automation of data workflows, reducing the risk of errors and inconsistencies.
Conclusion
Data platform layers must work seamlessly together to provide end-to-end data management capabilities and create a central location for organizational data.
This is exactly why organizations need to choose a platform that caters to their specific requirements to achieve optimal performance at the lowest cost. That said, whether you’re a startup or an established enterprise, it’s time to embrace the power of a data platform and unlock the full potential of your data. If you’re considering a shift to a data platform or want to learn more, contact us.
In the meantime, how about giving part two of our data platform series a read?
