The intelligence that powers real-time analytics, smart applications, and machine learning operations starts with data. Lots and lots of data! Getting data from everywhere to where your data team can use it for innovation and growth, starts with data ingestion.
What is Data Ingestion?
Data ingestion can be defined as the process of moving data from one or more sources into a target site and used for queries and analysis or storage. The data sources may include IoT devices, data lakes, databases, on-premise databases, SaaS applications, and other platforms which may have valuable data. From these sources, the data is ingested into platforms such as a data warehouse or data mart. A simple process of data ingestion takes data from a point of origin, cleans it up, and then writes it to a destination where it can be accessed, used, and analyzed by an organization.
The data ingestion layer is the bedrock of any analytical architecture. Downstream reporting and analytical systems rely heavily on consistent and accessible data. Data ingestion allows organizations to make valuable decisions from the ever-increasing volume and complexity of data they produce daily.
There are various ways of ingesting data into your data warehouse or data mart. Choosing the method which will work best for you depends on the design requirements and particular needs of your company.
Data ingestion technology offers various benefits, enabling teams to manage data more efficiently and gain a competitive advantage. Some of these benefits include:
- Data is readily available: Data ingestion helps companies gather data stored across various sites and move it to a unified environment for immediate access and analysis.
- Data is less complex: Advanced data ingestion pipelines, combined with ETL solutions, can transform various types of data into predefined formats and then deliver them to a data warehouse.
- Teams save time and money: It automates some of the tasks that previously had to be manually carried out by engineers, whose time can now be dedicated to other more pressing tasks.
- Companies make better decisions: Real-time data ingestion allows businesses to quickly notice problems and opportunities and make informed decisions.
- Teams create better apps and software tools: Engineers can use data ingestion technology to ensure that their apps and software tools move data quickly and provide users with a superior experience.
Why data ingestion matters
As the first stop at the beginning of any data pipeline, the data ingestion process is extremely important. These are the main reasons why:
Setting the stage for critical data operations
Once data is ingested, it can be cleaned, processed, deduplicated, virtualized, or propagated, based on the needs of a given data operation. These steps are necessary for proper data storage, warehousing, analytics, or application use. An effective data ingestion tool can be configured so that it prioritizes the intake of data from the most business-critical sources, helping this data be processed as efficiently as possible.
Facilitating data integration
Data ingestion also kick-starts the process of data integration – bringing together data from many sources, converting it to a uniform format if necessary, and presenting it as a comprehensive unified view. Ingesting data into a single platform that can be used by all departments also helps to limit the formation of data silos – which continue to be a common problem.
Streamlining data engineering operations
Many aspects of modern data ingestion are automated. Once they’ve been set up, these multi-step processes will run with little to no human intervention – unlike in years past, when data engineers sometimes had to wrangle data manually. Automation gives these professionals the freedom to address more mission-critical tasks and also accelerates the overall data engineering process.
Improving analytics and decision-making
For any data analytics project to be successful, it’s critical that data is consistently and readily available to analysts. The data ingestion layer directs this information to whatever storage medium is most appropriate for on-demand access, be that a data warehouse or a more specialized destination like a data mart. Also, ingesting data in the manner most appropriate for specific analyses – e.g., batch processing for daily expense reporting, or real-time ingestion for a vehicle’s ADAS data – is also an essential foundation for effective analytics.
Data ingestion on a single platform helps ensure that all business users can access and analyze high-quality data – which is vital for decision-making in the enterprise. The speed of real-time ingestion makes it particularly valuable as a foundation for analytics, leading to more valuable insights and better decisions.
Types of Data Ingestion
Data ingestion is collecting and preparing data from various sources in a data warehouse. It involves gathering, cleansing, transforming, and integrating data from disparate sources into a single system for analysis.
There are two main types:
- Real-time ingestion involves streaming data into a data warehouse in real-time, often using cloud-based systems that can ingest the data quickly, store it in the cloud, and then release it to users almost immediately.
- Batch ingestion involves collecting large amounts of raw data from various sources into one place and then processing it later. This type of ingestion is used when you need to order a large amount of information before processing it all at once.
Challenges companies face while ingesting data
Now that you are aware of the approaches data can be ingested into a medium, here is a list of problems that companies often face while ingesting data and how a data ingestion tool can help solve that challenge.
Maintaining data quality
The biggest challenge of ingesting data from any source is to maintain data quality and completeness. It is critical for business intelligence transactions that you will be performing on your data. However, since ingested data is not used for BI on an ad-hoc basis, data quality issues often go undiscovered. You can minimize this by using a data ingestion tool that provides added quality features.
Syncing data from multiple sources
Data is available in multiple formats in an organization. As the organization grows, more data will get piled up, and soon it will become hard to manage. Syncing all this data or ingesting it in a single warehouse is the solution. But since this data is available from multiple sources, extracting it can be a problem. This can be solved by data ingestion tools that offer multiple interfaces to extract, transform, and load the data.
Creating a uniform structure
To make business intelligence functions work properly, you will need to create a uniform structure by using data mapping features that can organize the data points. A data ingestion tool can cleanse, transform, and map the data to the right destination.
Data ingestion is a crucial part of data operations for many businesses. A refined and well-planned data ingestion process can be revolutionary for the businesses that implement it.
Ingesting data is a paramount feature for the growth of today’s companies with the avalanche of continuous data. Everything has to be ingested quickly and securely while being cataloged and stored. Once stored the data is leveraged by applications to help the business maintain a competitive advantage. At this point, you should know exactly what is required to implement data ingestion to help your organization leverage it to its competitive advantage.