Author: Keerthivasan santhanakrishnan

As the data industry evolves, data insights become increasingly accessible, forming the fundamental elements for various time-centric applications such as fraud detection, anomaly detection, and business insights. An essential and widely recognized component in unlocking the value and accessibility of this data is comprehending its nature and interrelationships and extracting meaningful values. 

Data modeling serves as the crucial blueprint akin to architectural plans for a house, defining how data is structured, modeled, and interconnected to facilitate informed decision-making efficiently. Additionally, data modeling elevates raw data collection to a level that is ready to be transformed into information, empowering the users to derive valuable insights. This article highlights the significance of data modeling, data storage, and ETL (Extract, Transform, Load) design tailored for downstream analytics. It draws insights from sample data obtained through the Binance crypto open API.

In the midst of our ever-expanding digital landscape, data management undergoes a metamorphic role as the custodian of the digital realm, responsible for the ingestion, storage, and comprehension of the immense volumes of information generated daily. At a broad level, data management workflows encompass the following phases, which are integral to ensuring the reliability, completeness, accuracy, and legitimacy of the insights (data) derived for business decisions. 

1. Data identification: Identifying the required data elements to achieve the defined objective.
2. Data ingestion: Ingest the data elements into a temporary or permanent storage for analysis.
3. Data cleaning and validation: Clean the data and validate the values for accuracy.
4. Data transformation and exploration: Transform, explore, and analyze the data to arrive at the aggregates or infer insights.
5. Visualization: Apply business intelligence over the explored data to arrive at insights that complement the defined objective.

Within these stages, Data Ingestion acts as the guardian of the data realm, ensuring accurate and efficient entry of the right data into the system. It involves collecting targeted data, simplifying structures if they are complex, adapting to changes in the data structure, and scaling out to accommodate the increasing data volume, making the data interpretable for subsequent phases. This article will specifically concentrate on large-scale Data Ingestion tailored for both batch and near real-time analytics requirements.