Data Ingestion Meaning

Data ingestion is the transportation of data from assorted sources to a storage medium where it can be accessed and analyzed by an enterprise. Data ingestion, also known as data acquisition or data intake, is typically performed on big data sets.

Data ingestion methods include batch processing and real-time processing. Depending on the system, the data can be ingested in either a raw format or a serialized format such as XML or JSON (JavaScript Object Notation).

Data Ingestion Process

The process includes extracting data from its source, deserializing it and storing it in a database. Once stored, the data can be analyzed with a variety of tools and software, including Hadoop, Apache Spark and Apache Storm.

BI (business intelligence) platforms often include data ingestion tools that can transfer data from multiple sources into a single location for analysis. Enterprise resource planning (ERP) software solutions also use data ingestion to combine information from different systems into one source for reporting, analytics and other tasks.

Data ingestion involves collecting data from various sources and bringing it into an organization's technology infrastructure or central location for further processing and management.

Data ingestion refers to the process of receiving, importing or transferring data for immediate use or storage in a database. There are many different types of big data sources that organizations can use to collect information. These include social media sites, mobile applications, e-commerce sites, web analytics tools and Internet of Things (IoT) devices. Organizations can also collect internal data from systems such as databases and customer relationship management (CRM) applications.

What Are The Benefits?

Data ingestion is inevitable especially as businesses and organizations begin to scale. Having a sound data ingestion strategy that is frequently reviewed is important to maintaining data integrity, as well as maintaining efficiency on a team. Some of the benefits of a sound data ingestion technology and strategy include:

• Saving time and money. By utilizing a data ingestion tool, time and resources from your data team (data analysts, engineers, etc) will be better allocated, since many of the tasks can be automated.
• Making data-informed business decisions. A data ingestion tool will be able to alert teams of KPI trends that may not be favorable for the business, and predict future trends that inform business decisions.
• Accessible data. Ensuring that company data is on one platform or environment not only makes data less complicated to understand, but also makes it easy to access for those who need it.

Common Techniques and Strategies

The proliferation of big data means that more and more companies need to be able to pull together information from across their entire business in order to drive actionable insights. Data ingestion helps them do this. Data ingestion is the foundation on which an effective big data strategy is built. It’s how organizations are able to unify enterprise-wide information so they can gain a holistic understanding of their customers, products, and processes.

In order to centralize all this information, however, you first have to be able to pull it together from multiple sources. This where data ingestion comes in.

Data ingestion strategies can include:

• ETL (extract, transform, load): This process extracts data from an OLTP database, transforms it (for example, by mapping data elements, removing incorrect data or merging two datasets) and loads it into another database.
• ELT (extract, load, transform): This process simply extracts and loads raw data before transforming it. With ELT, there is no need to transform and load the data immediately; instead these steps can be done at query time when needed. This means that raw data sits until it is queried, and then transformed onto another database.
• Real-time streaming: Data is processed as soon as it arrives at its destination. This approach is useful when there are time constraints such as when analyzing financial transactions

Examples

ETL: In this process, data is first extracted from its source, transformed to meet the requirements of the target system, and then loaded into the target system. This process is commonly used when the target system requires data to conform to a specific structure or schema.

Example: A company wants to migrate data from a legacy system to a new system. The legacy system stores data in a proprietary format, which is not compatible with the new system. The data needs to be extracted from the legacy system, transformed into a format that is compatible with the new system, and then loaded into the new system.

‍ELT: In this process, data is first extracted from its source and loaded into the target system without any transformation. The transformation process is then applied to the data in the target system. This process is commonly used when the target system is flexible and can handle unstructured or semi-structured data.

Example: A company wants to analyze data from various sources, including social media and weblogs. The data is extracted from these sources and loaded into a data lake. The transformation process is then applied to the data in the data lake to make it accessible for analysis.

Real Time Streaming: exampes include but are not limited to analyzing social media data to track customer sentiment, monitoring website traffic in real-time, processing sensor data from IoT devices, and detecting anomalies in financial transactions.

Learn More With Secoda

Secoda integrates with the modern data stack and creates a homepage for your date. Regardless of your method of data ingestion, Secoda automatically indexes every part of your data stack and creates a single source of truth for data-driven organizations to power their business decisions.

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