What is Data Ingestion? Types, Challenges and Best Practices

Import, process, and transform data for later use and security analysis. Learn how data ingestion can save your organization and benefit your users.

If you ever wanted to extract insights for your business or make good decisions, you may have encountered some roadblocks or gone through slip-ups. Every hiccup you experience, at least on an information level, is due to poor data ingestion practices. Data is the bloodline of your business and without good data ingestion workflows and tools, you cannot sustain your organization.

Data comes from various sources, formats, and in different sizes. Ingestion is at the heart of it all. You can’t prepare reports, documents, or make critical decisions without having it set up properly. In this guide, we will explore data ingestion definitions, data ingestion frameworks, and talk about  some of the various processes businesses use. You’ll get an idea about data ingestion meanings, pipelines, challenges, practices, and so much more. Let’s go!

What is Data Ingestion?

Data ingestion is a process where you collect, import, and load data from multiple and diverse sources. You put this data into a centralized storage for later processing and analysis. Data ingestion makes up the foundation of every data pipeline.

Here's why:

• It is the very first step that ensures your info is available, accessible, and ready to use
• You harvest raw data and transfer that to target systems. You can move that data to date warehouses, data lakes, or any other storage facilities.

The purpose of data ingestion is:

• To prepare and make your data available for business intelligence and analysis
• To Improve data quality - this includes cleaning up, validating, and transforming your data. This also ensures data accuracy and consistency.
• Automate workflows to make it easier to flow your data from source to destination.

Why Is Data Ingestion Important?

Data ingestion is important for organizations because it gives them a competitive advantage. Companies do market research using the data, uncover the latest trends, and find hidden opportunities by utilizing its applications. Today’s digital environments are rapidly evolving and data landscapes are changing; this means businesses need to keep up with emerging trends, including having the ability to accommodate any changes in data volumes, velocities, and performance.

Customers generate high volumes of data exponentially and have ongoing demands. Data ingestion helps provide them a comprehensive view of business operations. It ensures transparency, integrity, accountability, and availability, thus allowing businesses to boost their overall credibility and reputation in industries.

Data Ingestion vs ETL

ETL is an acronym for “Extract, Transform, Load,” and it refers to the process of synthesizing data for querying, structuring, and warehousing purposes. Modern data ingestion definition focuses on inputting data into systems; ETL is more concerned with processing and organizing it. ETL optimizes unstructured data and makes it suitable for use in data analytics.

The following are the key differences between data ingestion and ETL:

Data Ingestion	ETL
Data ingestion can be a fragmented process and deals with challenges such as overlap, duplicates, and data drifts.	ETL addresses data quality and validity requirements and improves business operations by high volumes of unstructured data. It resolves any data ingestion issues faced across the pipeline.
Data ingestion focuses on the real-time import and analysis of raw data	ETL focuses on applying a series of transformations before loading the end result
Mostly compatible with streaming data	ETL is best suited for batch data
Data ingestion is a push process	ETL is a pull process
Data ingestion reads high volumes of raw data in different formats from across multiple sources. It ingests it into the Data Lake for further analysis.	ETL aggregates, sorts, authenticates and audits the data before loading it into a warehouse for further operations

ETL is widely used to migrate data from legacy systems onto the IT infrastructure. ETL solutions can transform data into new architectures and load it into new systems. Data ingestion is more ideal for monitoring, logging, and business analysis needs. It can be used alongside data replication to store sensitive data across multiple locations and ensure high availability. The main difference between data ingestion and ETL is that - data ingestion collects data from different sources, whereas ETL transforms and restructures it for use in different applications.

Types of Data Ingestion

There are different types of data ingestion systems and they are as follows:

• Batch ingestion - This gathers and holds your data in a temporary storage. You schedule job runs to process and load it into final destinations.
• Streaming ingestion - Streaming ingestion collects and processes data as soon as it generates. There are no delays and it is best for gaining time-sensitive insights.
• Hybrid ingestion - Hybrid ingestion blends batch and streaming ingestion processes. It involves micro-batching and some models may use a Lambda architecture. It's great for ensuring historical data accuracy, low latency, and real-time data streaming.
• Incremental vs full ingestion - Incremental ingestion depends on the last ingestion cycle. It relies on Change Data Capture (CDC) mechanisms to identify and extract new or modified data from source databases. Full ingestion ingests entire datasets from source systems and uses it for one operation.

Data Ingestion Process

The data ingestion process entails the following phases:

1. Data Discovery

Data discovery is an exploratory phase where an organization's what type of data is available, where it’s coming from, and how it can be used for business benefits. It aims at acquiring clarity about the data landscape, its quality, structure, and potential function.

2. Data Acquisition

Data acquisition is the next step after data discovery. It involves collecting the data from selected sources once it’s been identified. Data sources can be varied and range from APIs, databases, spreadsheets, and electronic documentation.

Data acquisition includes sorting through high volumes of data and can be a complex process since it involves dealing with various formats.

3. Data Validation

Data validation involves checking the data for consistency and accuracy. It improves data reliability and enhances trustworthiness. There are different types of data validation such as range validation, uniqueness validation, data type validation, etc. The goal of validation is to ensure that data is clean, usable, and ready to be deployed for the next steps.

4. Data Transformation

Data transformation is the process of converting data from a raw format into one that is more desirable and suitable for use. It involves different processes such as data standardization, normalization, aggregation, and others. The transformed data is meaningful, easy to understand, and ideal for analysis. It can provide valuable insights and serve as a great resource.

5. Data Loading

Data loading is the final phase of the data ingestion workflow where it culminates into the end. Data transformed is loaded into a warehouse where it can be used for additional analysis. The processed data can also be used to generate reports, be reused elsewhere, and is ready for use in business decision-making and insight generation.

How a Data Ingestion Pipeline Works?

Your pipeline will take your raw data from various sources and prep it for analysis. You can also use it to support SQL-based analytics and process other workloads.

Here are its different stages:

• Data discovery - You understand the data landscape and get an overview of your organization's data. This is mainly the exploratory phrase and you get an idea about your data structures, quality, and basic groundwork for doing successful ingestion.
• Data acquisition - This is the part of your pipeline architecture that collects your data. You retrieve data from multiple sources.
• Data validation - You ensure the accuracy and consistency of data in this phase. The data is fact-checked for errors and missing values as well. Basically, you make the data more reliable and ensure both its integrity and uniqueness.
• Data transformation - You convert your validated data and normalize it to remove redundancies. You also aggregate and standardize the data to ensure consistent formatting. You make your data easier to analyze in the process.
• Data loading - You load your transformed data onto a specific location. This can be a data ingestion lake or a data warehouse. You can load in batches or real-time the full volume. This completes your data ingestion pipeline and from here, your focus shifts to generating valuable business intelligence from it.

Data Ingestion Framework

A data ingestion framework is a workflow designed to transport data from various sources into a storage repository for analysis and additional use. The data ingestion framework can be based on different models and architectures. How quickly the data will be ingested and analyzed will depend on the style and function of the framework.

Data integration is closely connected to the concept of the data ingestion framework, however, it is not the same. With the rise of big data applications, the most popular framework being used for data ingestion is the batch data ingestion framework. It involves batch processing data groups and transporting them into data platforms periodically, in batches. Fewer computing resources are needed for this and there are options to ingest data in real-time by using data ingestion streaming frameworks.

Advantages of Data Ingestion

Data ingestion helps businesses learn about their competitors and better understand the market. The data they gather will be analyzed for crafting higher quality products and services for consumers. Below are the most common advantages of data ingestion for organizations:

1. Holistic Data Views

Data ingestion can provide more holistic views of an organization’s data security posture. It ensures that all relevant data is available for analysis, eliminates redundancies, and prevents false positives. By centralizing data from various sources into repositories, organizations can get a complete view of the industrial landscape, identify trends, and understand the nuances of changing consumer behaviors.

2. Data Uniformity and Availability

Data ingestion eliminates data silos across the organization. It helps businesses make informed decisions and provide up-to-date statistics. Users derive valuable insights and can optimize their inventory management and marketing strategies in the process. Ensuring all-round data availability also rapidly enhances customer service and business performance.

3. Automated Data Transfers

Using data ingestion tools can enable automated data transfers. You can collect, extract, share, and send the transformed information to relevant parties or users. Data ingestion allows businesses to free up time for other important tasks and greatly enhances business productivity. Any valuable information gained from the data translates to improved business outcomes and can be used to seal gaps in marketplaces.

4. Enhanced Business Intelligence and Analytics

Real-time data ingestion allows businesses to make accurate by-the-minute predictions. Businesses can provide superior customer experiences by conducting forecasts and saving time by automating various data management tasks. Ingested data can be analyzed using the latest business intelligence tools and business owners can extract actionable insights. Data ingestion makes data uniform, readable, less prone to manipulation, and accessible to the right users at the right moments.

Key Challenges of Data Ingestion

Although data ingestion has its pros, there are key challenges faced during the process. The following is a list of the most common ones:

1. Missing Data

There is no way of knowing whether the data ingested is complete and contains all components. Missing data is a huge problem experienced by organizations when ingesting data from across multiple locations. Lack of quality data, inconsistencies, inaccuracies, and major errors can negatively impact data analysis.

2. Compliance Issues

Importing data from several regions can raise compliance concerns for organizations. Every state has different privacy laws and restrictions regarding how their data is used, stored, and processed. Accidental compliance violations can increase the risk of lawsuits, reputation damages, and lead to other legal repercussions.

3. Job Failures

Data ingestion pipelines can fail and there is a high risk of orchestration issues when multi-step complex jobs are triggered. Each vendor has its own policies and some do not plan for mitigating data losses. Duplicate data can result from human or system errors. There is a possibility of the creation of stale data as well. Different data processing pipelines can add complexity to architectures and require the use of additional resources.

What are the Data Ingestion Best Practices?

Here is a list of the top data ingestion best practices for organizations:

• Automatically ingesting data and setting up the right workflows for it is a good practice.  Use the best data ingestion tools and processes to accomplish this. Good tools will use event-based triggers to automate repeatable ingestion tasks and help save orchestrators' times, while reducing human error.
• If you can't decide between streaming vs batch data ingestion, then make data SLAs. It should cover information like what your business needs, your data expectations, who gets affected, and how to know when your agreement is met or violated.
• Decouple operational and analytical databases so that they don't cascade into one another. Also check for data quality at ingestion points.  Make tests for every instance of a bad data pipeline that isn't scalable. You should also create and use data circuit breakers to stop ingestion processes if the data doesn't meet certain quality checks.
• Use data observability solutions to provide broader coverage for your modern data stack. This will enhance data reliability and business intelligence. Data observability will complement your data ingestion pipeline and definitely help.

Data Ingestion Use Cases

Here are four common data ingestion use cases:

1. Data warehousing - This is where the data is stored, kept up-to-date, and utilized to automate data ingestion processes. Data warehouses leverage real-time streams and micro-batching ingestion frameworks. They also verify, audit, and reconcile data.
2. Business intelligence and analytics - Your business intelligence strategy is influenced by your data ingestion process. You can make data-driven business decisions and make use of actionable insights at any moment to benefit your revenue streams, customers, and markets.
3. Machine learning - Machine learning in data ingestion lays the foundation for data classification and regression across both supervised and unsupervised learning environments. Models in machine learning pipelines can be trained to provide higher-quality outputs and be integrated with specialized tools.
4. Customer data onboarding - Customer data onboarding can be done manually or in ad-hoc mode; data ingestion can provide loads of valuable resources to new users and strengthen business relationships.

The Role of SentinelOne in Data Ingestion

SentinelOne Singularity™ AI SIEM is built for the autonomous SOC. It secures your organization with the industry's fastest AI-powered open platform for all your data and workflows.

Built on the SentinelOne Singularity™ Data Lake, it speeds up your workflows with Hyperautomation. It can offer you limitless scalability and endless data retention. You can filter, enrich, and optimize the data in your legacy SIEM. It can ingest all excess data and keep your current workflows.

You can stream data for real-time detection and drive machine-speed data protection with autonomous AI. You also get greater visibility for investigations and detections with the industry’s only unified console experience.

It is schema-free and no-indexing, and is Exabyte scale which means it can handle any data loads. You can easily integrate your entire security stack. It can ingest both structured and unstructured data, and is OCSF natively supported. You can also ensure consistent and effective threat responses with its automated incident response playbooks. Reduce false positives, alert noise, allocate resources better, and improve overall security posture today.

Conclusion

Good data ingestion practice is the backbone of every modern organization. Without high-quality data, integrity, and assurance, businesses cannot function effectively nor win in today’s competitive landscape. To capitalize on the innovation of analysis and make the most of insights extracted, strong data ingestion workflows are vital. Businesses can use dedicated data ingestion solutions or dynamic integration tools to streamline data processing and boost revenue growth.

You can sign up for a free demo with SentinelOne and learn how we can help you elevate your data pipelines.

FAQs

What is data ingestion vs data integration?

Data ingestion is about collecting the data for processing and analysis. Data integration focuses on applying a series of transformations and storing the transformed data in a warehouse for further usage.

How to choose a data ingestion tool?

The key factors you need to consider when deciding on a data ingestion tool are – interoperability, user-friendliness, processing frequency, interface type, security levels, and budget.

What is the difference between data collection and ingestion?

Data collection collects only raw data. Data ingestion collects, prepares, and processes the raw data for further analysis. Data collection is a one-time process while data ingestion is automated, ongoing, and involves collecting data from a variety of sources.

What is API data ingestion?

API data ingestion involves the use of a REST API and leverages two common interaction patterns: bulk and streaming. You can use near real-time ingestion APIs to insert third-party data into metrics, logs, events, alarms, groups, and inventories. API data ingestion is best suited for enhancing data accessibility, reliability, and standardizing it. They are faster and more scalable, being capable of supporting variable attribute modifications.