What is data ingestion?
Data ingestion helps users import large data files from different sources to a single medium, such as a data warehouse or database. This data is collected, cleansed, and converted to a uniform format using extract, transform, and load (ETL) processes.
Since modern organizations process large volumes of data, they have to prioritize their sources for successful data ingestion. Big data exists in different formats in various locations within an organization, and it’s challenging to ingest data quickly and process it effectively when it’s so dispersed.
Many vendors provide data preparation software to accomplish this goal and customize the platform for different computing environments and applications.
Types of data ingestion
Depending on the company objectives, IT environment, and financial constraints, companies can choose one of these types:
- Real-time data ingestion obtains and transfers data from source systems in real time using tools like change data capture (CDC). CDC continuously checks transactions and transfers modified data without affecting the workload on the database.
- Batch-based data ingestion transfers data in batches at set intervals. Data collection methods used by this type of data ingestion include basic schedules, trigger events, and other logical ordering. When businesses need to collect specific data points daily or don't require data for real-time decision making, batch-based ingestion is helpful.
- Lambda architecture-based data ingestion makes data available for querying with minimum delays. Three layers, batch, serving, and speed, work in parallel to facilitate this. The first two layers index data in batches, while the speed layer picks up the remaining data and indexes it instantly, making it available for querying in real time. For example, think of a search engine. A crawler indexes pages periodically or as per order. At the same time, it can index news pages almost instantaneously. This makes news and evergreen information available simultaneously.
Benefits of data ingestion
Data ingestion is a common technique in enterprises due to the volumes of data it generates and processes. It offers various benefits to businesses, like:
- Data availability: The process makes data available across organizations and enables easier access. Data is readily available for further analysis or downstream application, especially for data-centric departments.
- Simplified process: Data ingestion allows collecting and cleaning data from massive sources into a consistent format easily.
- Low cost: Data ingestion cuts costs and saves time compared to manual data aggregation.
- Cloud-based storage: Larger data volumes in raw form are stored in the cloud, enabling easy access.
- Data transformation: Before sending information to the target system, modern data pipelines using ETL tools transform the vast range of data types from various sources, including databases, Internet of Things (IoT) devices, software as a service (SaaS) applications, and data lakes, into a predefined structure and format.
- Collaboration: Every data pipeline has a limited scope for ingesting data. The pace at which data comes in is much higher. Automated data ingestion tools configured with relevant parameters based on a team’s requirements provide them more flexibility and agility to offer a better customer experience. It reduces human error and makes data available through a single pipeline, enhancing accessibility and collaboration.
Data ingestion use cases
Organizations worldwide use data ingestion effectively as a crucial component of their data pipelines. Below are some real-world industry and architectural use cases of data ingestion.
- In big data analytics, where data is handled using distributed systems, ingesting massive volumes of data from numerous sources frequently is necessary.
- Internet of Things systems often use data ingestion to gather and process data from several linked devices.
- E-commerce businesses use data ingestion to load data from various sources, such as website analytics, customer transactions, and product catalogs.
- Fraud detection systems use data ingestion to import and process data from different sources, like transactions, consumer behavior, and third-party data feeds.
- Personalization recommendations require data ingestion to import data from various sources, including website analytics, customer interactions, and social media data.
- Supply chain management leverages data ingestion to import and process supplier, inventory, and logistics data from several sources.
Data ingestion vs. ETL
Data ingestion refers to tools and processes that collect data from different sources and group it for immediate use or future analysis and storage.
ETL, or extract, transform, and load, is a technique that can be used for data ingestion. Here, extract refers to collecting data. Transform refers to operations carried out on the data to prepare it for use or storage. For instance, data may be sorted, filtered, or integrated with information from another source. Load refers to the volume of data supplied to a target destination where it can be utilized.
ETL transfers data to the target site in batches regularly. However, data ingestion does not necessarily operate in batches only. They can provide real-time processing with streaming computation, allowing data sets to be continuously updated.
Learn more about the best ETL tools available to ensure seamless data management.
