Data Ingestion

What Is Data Ingestion?

Data ingestion is the process of collecting data from its source(s) and moving it to a target environment where it can be accessed, used, or analyzed. Data sources range from data lakes, IoT devices, on-premises or cloud databases, and SaaS apps, among others. Targets often include cloud data warehouses, cloud data lakes, or data marts.

Data Ingestion Types

The backbone of any analytics architecture is the data ingestion layer. There are several types of data ingestion, and the design of a particular data ingestion layer can be based on various models or architectures.

Batch-Based Data Ingestion

Batch-based data ingestion, the most common kind of data ingestion, is the process of collecting and transferring data to a destination system in batches, usually according to schedules, trigger events or conditions, or any other logical ordering. Organizations use batch-based ingestion when they need to collect specific data points on a regular basis or perform ad hoc queries, but don’t need real-time data for decision-making.

Real-Time Data Ingestion

Real-time data ingestion is where data is sourced, manipulated, and loaded as soon as it’s created or recognized by the data ingestion layer. Organizations use real-time data ingestion for time-sensitive use cases when continually refreshed data is critical—such as stock market trading or power grid monitoring.

Lambda Architecture-Based Data Ingestion

Lambda architecture-based data ingestion combines batch and real-time data ingestion. It consists of batch, serving, and speed layers. The first two layers index data in batches, while the speed layer instantaneously indexes data that has yet to be picked up by slower batch and serving layers. This ongoing hand-off between different layers ensures that data is available for querying with low latency.

Why Is Data Ingestion Important?

Data ingestion is important because it helps organizations make sense of the ever-increasing volume, variety, and complexity of data. Data must be ingested before it can be digested by analysts, line-of-business managers, decision-makers, applications, or machine learning models. To make better, more educated decisions, organizations need access to all their data sources for analytics and business intelligence (BI). Downstream reporting and analytics systems rely on consistent and accessible data, and data ingestion makes this possible.

Automated data ingestion can help organizations operate more efficiently. By automating this process, organizations can eliminate tedious, manual tasks, saving time and money and allowing limited technical resources to be dedicated to other, high-value tasks. Engineers can use automated data ingestion technology to ensure that their apps and software tools move data quickly and provide users with a superior experience.

How Does Alteryx Enable Data Ingestion?

Alteryx streamlines data ingestion, creating a flexible environment that operates seamlessly within end-to-end analytic workflows and integrates fully into modern tool chains. Organizations use Alteryx to automate the process of ingesting, transforming, and delivering data from source to target, eliminating tedious, labor-intensive manual data ingestion workflows.

This intelligent, collaborative, self-service data engineering cloud platform facilitates data ingestion by making it easier to:

• Connect to data from any source. Designer Cloud offers universal data connectivity to a wide range of data sources, making it faster and easier to connect to and ingest any data. With a self-service architecture, Alteryx provides flexible and seamless access to data and supports connectivity to cloud storage, cloud data warehouses, and files.
• Transform raw data into ready-to-use data across the organization. Designer Cloud makes data useful and understandable to users of any skill level, regardless of its source, target, or use. Using Designer Cloud visual interface, organizations can leverage predictive data transformation techniques to detect and resolve complex data patterns and transform these patterns into consumable data for analytics and applications.
• Deploy and automate data pipelines in minutes. Designer Cloud makes it easier to deploy and automate data pipelines from source to destination, allowing users to schedule and automate their data workflows at scale.