Inhouse Data Flow — Building and Maintaining

Inhouse Data Flow is the process of managing data within a company. Building your own inhouse data storage is crucial for subsequent analysis and report generation.

Typically, in e-commerce, there is always a lot of data from various sources and platforms: marketplaces, sales, customers, statistics, logistics. Proper organization of data flow allows you to create an efficient system for working with data and subsequent processing into BI reports.

Inhouse Data Flow consists of and is organized into the following parts:

A centralized repository that allows you to store raw data from various sources. Flexibility in storing different types of data, scalability in volume, and the ability to store data for further analysis and processing are important. Data can be structured or unstructured.

In e-commerce, data can include: orders, customer data, product data, order and customer demographics, logs, price history, etc. Depending on the volume, data can be stored in the cloud (Amazon S3, Google Cloud Storage).

Generally, minimally processed data is stored in the Data Lake, which can be in the form of text files, JSON, CSV files, zip archives. It is important to save all collected and minimally processed data. Often not all data is needed immediately, but as data is collected, it can become the subject of future analysis.

Data is added to the Data Lake through the process of Data Extraction & Integration. Data is collected from various APIs, databases, and parsing.

An organized data repository optimized for fast query execution and analytics. Unlike Data Lake, all data in the Data Warehouse is structured and organized. Data is integrated from various sources, and the structure is optimized for high-speed query execution and support for business analytics and reporting.

Typically, this is a database, and depending on the volume and goals, it can be a separate database or a cloud-based and distributed one (Amazon Redshift, Google BigQuery).

Data is added to the Data Warehouse through the Integration process, usually after the Data Extraction process. During analysis, data is cleaned and structured before being moved to the Data Warehouse.

Reports generated using data analytics tools that provide data visualization and help in decision-making.

For example, in e-commerce, BI Reports are used to analyze key performance indicators (KPIs), identify sales trends, and evaluate marketing campaigns. Higher-level BI reports can consolidate all business metrics and include analytics across all channels, showing the overall business performance.

To create BI reports, data from the Data Warehouse is formed using SQL queries or Python scripts and is then shaped in the used BI system (Tableau, Looker Studio, Power BI) or in Google Spreadsheets.

Our team has significant experience in building all stages of Inhouse Data Flow at various levels using cloud services like Amazon Web Services (from 5 to 100 million sales per year), ensuring cost-effective infrastructure and support from our side, along with a high level of reliability and security for your data storage.