What is data ingestion?

Data sources include financial systems, third-party data providers, social media platforms, IoT devices, SaaS apps, on-premises business applications like enterprise resource planning (ERP) and customer relationship management (CRM).

These sources contain both structured and unstructured data. Once data is ingested, it can be stored in data lakes, data warehouses, data lakehouses, data marts, relational databases and document storage systems. Organizations ingest data so it can then be used in business intelligence tasks but also for machine learning, predictive modeling and artificial intelligence applications.

Many data ingestion tools automate this process organizing raw data into appropriate formats for efficient analysis by data analytics software. Data ingestion typically requires expertise in data science and programming languages like Python. The data is sanitized and transformed into a uniform format by using an extract, transform, load (ETL) process or extract load transform process (ELT), to manage the data lifecycle effectively.

With diverse and numerous big data sources, automation software helps tailor the ingestion process to specific environments and applications. Often including data preparation features for immediate or later analysis by using business intelligence and analytics programs.

Data ingestion is the first step in processing data and extracting value from the large amount businesses collect today. A well-planned data ingestion process safeguards the accuracy and reliability of the data feeding into the analytics engine, which is vital for data teams to perform their functions effectively. There are three key reasons why data ingestion is essential:

Data ingestion is the process of taking raw data from various sources and preparing it for analysis. This multistep pipeline ensures that the data is accessible, accurate, consistent and usable for business intelligence. It is crucial for supporting SQL-based analytics and other processing workloads.

Data discovery: The exploratory phase where available data across the organization is identified. Understanding the data landscape, structure, quality and potential uses lays the groundwork for successful data ingestion.

Data acquisition: Once the data sources are identified, data acquisition involves collecting the data. This can include retrieving data from many sources, from structured databases and application programming interfaces (APIs) to unstructured formats like spreadsheets or paper documents. The complexity lies in handling the variety of data formats and potentially large volumes and safeguarding data integrity throughout the acquisition process.

Data validation: After acquiring the data, validation guarantees its accuracy and consistency. Data is checked for errors, inconsistencies and missing values. The data is cleaned and made reliable and ready for further processing through various checks like data type validation, range validation and uniqueness validation.

Data transformation: Here is where validated data is converted into a format suitable for analysis. This might involve normalization (removing redundancies), aggregation (summarizing data) and standardization (consistent formatting). The goal is to make the data easier to understand and analyze.

Data loading: The final step places the transformed data into its designated location, typically a data warehouse or data lake, where it's readily available for analysis and reporting. This loading process can be done in batches or in real-time, depending on the specific needs. Data loading signifies the completion of the data ingestion pipeline, where the data is prepped and ready for informed decision-making and generating valuable business intelligence.

When ingesting data, ensuring its quality is paramount.

• Handling missing values: Techniques include imputation (replacing missing values with statistical measures), deletion (removing records or fields with missing values if they represent a small portion of the dataset) and prediction (by using machine learning algorithms to predict and complete missing values based on other available data).
• Identifying and correcting outliers: Common techniques include statistical methods such as using z-scores or the interquartile range (IQR) method to detect outliers. Visualization tools like box plots or scatter plots and applying log or square root transformations to reduce the impact of outliers.
• Standardizing data formats: Standardization helps to ensure consistency across the dataset, facilitating easier analysis. This includes uniform data types, normalization and code mapping.

Data governance helps maintain data quality during ingestion by establishing policies and standards for data handling. This ensures that there is accountability through defined roles and responsibilities. Implementing metrics and monitoring systems to track and address issues, facilitating compliance with regulations like GDPR or HIPAA and promoting consistency by standardizing data definitions and formats.

Data ingestion breaks down data silos and makes information readily available to everyone in the organization who needs it. By automating data collection and by using cloud storage, data ingestion safeguards data security and access to valuable insights.

Data ingestion breaks down data silos, making information readily available across various departments and functional areas. This fosters a data-driven culture where everyone can use insights gleaned from the company’s data ecosystem.

Data ingestion simplifies the often-complex task of collecting and cleansing data from various sources with diverse formats and structures. Businesses can streamline data management processes by bringing this data into a consistent format within a centralized system.

An effective low latency data ingestion pipeline can handle large amounts of data at high speeds, including real-time ingestion.

Businesses reduce the time and resources traditionally required for manual data aggregation processes by automating data collection and cleansing through data ingestion. Also, as-a-service data ingestion solutions can offer further cost benefits by eliminating the need for upfront infrastructure investment.

A well-designed data ingestion process empowers businesses of all sizes to handle and analyze ever-growing data volumes. Scalability is essential for companies on a growth trajectory. The ability to effortlessly manage data spikes makes sure that the businesses can continue to use valuable insights even as their data landscape expands.

By using cloud storage for raw data, data ingestion solutions offer easy and secure access to vast information sets whenever needed. This eliminates the constraints of physical storage limitations and empowers businesses to use their data anytime, anywhere.

Data ingestion, extract, transform, load (ETL) and extract, load, transform (ELT) serve a common goal but differ in their approaches.

• Data ingestion: Data ingestion encompasses all the tools and processes responsible for collecting, extracting and transporting data from diverse sources for further processing or storage.
• ETL: Extract, transform and load is the process by which data is extracted from its source system transformed it to meet the target system's requirements. And then loaded it into the designated data warehouse or data lake.
• ELT: Extract, load and transform is the process by which data is extracted from its source. The raw data is loaded into the target system and then transformed on-demand and as needed for specific analyses. ELT uses the capabilities of cloud platforms to handle large volumes of raw data and perform transformations efficiently

Data ingestion and data integration serve distinct purposes within the data pipeline.

Data ingestion: Acts as the entry point for data from various sources, with the primary concern being the successful transfer of data, with minimal transformation to maintain the data's original structure.

Data integration: Focuses on transforming and unifying data from multiple sources before feeding it into a target system, typically a data warehouse or data lake. Data integration might involve data cleansing, standardization and enrichment to ensure consistency and accuracy across the entire dataset.

Data ingestion encompasses various methods for bringing data from diverse sources into a designated system.

This ingestion method involves accumulating data over a specific period (daily sales reports, monthly financial statements) before processing it in its entirety. Batch processing is known for its simplicity, reliability and minimal impact on system performance, as it can be scheduled for off-peak hours. However, it's not ideal for real-time applications.

This method offers instant insights and faster decision-making by ingesting data the moment that it's generated, enabling on-the-spot analysis and action. This method is perfect for time-sensitive applications like fraud detection or stock trading platforms where immediate decisions are paramount.

Stream processing is very similar to real-time processing, except that it takes the ingested data and analyzes it continuously as it arrives. Both real-time and stream processing demand significant computing power and network bandwidth resources.

The microbatching method strikes a balance between batch and real-time processing. It ingests data in small, frequent batches, providing near real-time updates without the resource constraints of full-scale real-time processing. Careful planning and management are necessary to optimize the tradeoff between data freshness and system performance.

This ingestion method combines both batch and real-time processing, by using the strengths of each to provide a comprehensive solution for data ingestion. Lambda architecture allows for processing large volumes of historical data while simultaneously handling real-time data streams.

Data ingestion tools offer diverse solutions to cater to various needs and technical expertise.

Open source tools: Tools that provide free access to the software's source code, giving users complete control and the ability to customize the tool.

Proprietary tools: Solutions that are developed and licensed by software vendors, they offer prebuilt functions and varied pricing plans but might come with vendor lock-in and ongoing licensing costs.

Cloud-based tools: Ingestion tools that are housed within a cloud environment, simplifying deployment and maintenance and offering scalability without the need for upfront infrastructure investment.

On-premises tools: These tools are installed and managed on a local or private cloud network, providing greater control over data security but requiring investment in hardware and ongoing IT support.

In balancing needs and expertise, several approaches exist for building data ingestion pipelines:

Hand-coded pipelines: These bespoke pipelines offer maximum control but require significant development expertise.

Prebuilt connector and transformation tools: This approach provides a user-friendly interface but necessitates managing multiple pipelines.

Data integration platforms: This platform offers a comprehensive solution for all stages of the data journey but demands development expertise for setup and maintenance.

DataOps: This approach is about promoting collaboration between data engineers and data consumers and automating portions of the data ingestion process to free up valuable time.

While foundational for data pipelines, the data ingestion process is not without its complexities.

Data security: Increased exposure elevates the risk of security breaches for sensitive data. Adhering to data security regulations adds complexity and cost.

Scale and variety: Performance bottlenecks can arise due to the ever-growing volume, velocity and variety of data.

Data fragmentation: Inconsistency can impede data analysis efforts and complicate creating a unified data view. When the source data changes without an update in the target system, it causes schema drift, which can disrupt workflows.

Data quality assurance: The intricate nature of data ingestion processes can compromise data reliability.

Data ingestion serves as the foundation for unlocking the potential of data within organizations.

Data ingestion solutions allow businesses to collect and transfer various data into a centralized cloud data lake target. High-quality data ingestion is paramount in this scenario, as any errors can compromise the value and reliability of the data for downstream analytics and AI/machine learning initiatives.

Organizations migrating to the cloud for advanced analytics and AI initiatives often face challenges related to legacy data, siloed data sources and increasing data volume, velocity and complexity. Modern data ingestion solutions often provide code-free wizards that streamline the process of ingesting data from databases, files, streaming sources and applications.

Data ingestion solutions can accelerate data warehouse modernization by facilitating the mass migration of on-premises databases, data warehouses and mainframe content to cloud-based data warehouses. Using Change Data Capture (CDC) techniques with data ingestion keep the cloud data warehouse constantly updated with the latest information.

Real-time processing of data streams opens doors to new revenue opportunities. For instance, telecommunication companies can use real-time customer data to optimize sales and marketing strategies. Similarly, data collected from IoT sensors can enhance operational efficiency, mitigate risks and generate valuable analytical insights.

To unlock the power of real-time analytics, data ingestion tools enable the seamless integration of real-time streaming data (clickstream data, IoT sensor data, machine logs, social media feeds) into message hubs or streaming targets, allowing for real-time data processing as events occur.