What is Data Ingestion? Types, Challenges and Best Practices

Data-driven decisions can make or break a company’s business potential. Insights derived from Big Data can shape the future growth of global organizations. With the stakes being high, it is imperative to gather data from all applications, channels, and have high-performance data ingestion workflows in place. Data ingestion is used to collect and move the processed data into storage. It is a critical component of every data pipeline and is designed to enable teams to get near-instant access to the latest data without facing any integrity challenges or discrepancies. Good data ingestion ensures high-quality data, confidentiality, availability, and scalability.

In this guide, we will discuss what is data ingestion, how it works, different types, benefits, and more.

What is Data Ingestion?

Data ingestion is the process of importing, extracting, and transforming data for later use in databases by organizations. In modern business, the transformed data is processed either manually or automatically for performing a variety of tasks.

Ingested data can come from different sources and formats. The data may be presented in structured or unstructured forms before it is collected and cleaned up. Data ingestion is done across a variety of channels such as – social media feeds, internal logs and reports, commercial feeds, and even real-time feeds as in the likes of the Internet of Things (IoT) or connected devices.

The main purpose of data ingestion is to extrapolate information and convert it into a usable format. The data organized is used for different applications in analytics, machine learning, data processing pipelines, and others.

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:

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 mainly two types of data ingestion workflows and they are:

1. Streaming

Streaming is real-time data ingestion where captured data from live sources is processed in real-time. All changes are automatically synced when made without affecting current database workloads. Streaming is apt for time-sensitive tasks and powers operational decisions via rapidly delivering insights.

2. Batch

When data is processed and moved in batches, usually on a scheduled basis, it is referred to as batch data ingestion. Analysts use batch data ingest to collect specific types of data sets from CRM platforms on the same days of the month. This type of data collection does not impact real-time business decision-making. It is primarily used to collect specific data points for deeper analysis at periodic intervals.

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.

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?

The following are the best data ingestion practices for organizations:

• Organizations should adopt a data mesh model to collect, process data, and gather real-time insights; it also ensures reliable and accurate data processing.
• Gather data use case specifications from your clients. It is an excellent practice to create Data SLAs and sign them before rendering business services.
• Apply data quality checks during the ingestion phase itself. Create tests for every pipeline that are scalable, flexible, and deploy circuit breakers. Leverage data observability to quickly detect incidents and resolve them before they escalate.
• Back up your raw data before performing ingestion. Make sure the data conforms to compliance standards prior to processing it.
• For data issues, you can add alerts at the source. Set realistic timelines for your ingestion pipelines and have proper tests in place. All data ingestion pipelines should be automated with all necessary dependencies. You can use orchestration tools to synchronize different pipelines.
• It is extremely important to document your data ingestion pipelines. Create templates for framework reuse and pipeline development. The increased velocity when ingesting new data will benefit your business.

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™ Data Lake transforms your raw data into actionable insights and skyrockets business performance. Users can conduct real-time incident investigations and responses with our unified AI-driven data lake.

Singularity™ Data Lake is a rapid data ingestion solution that performs lightning-fast queries and near real-time analytics. It can ingest data from any first or third-party sources using pre-built connectors and automatically normalize using OCSF standards. Automated workflows enhance AI-assisted analytics and users can connect disparate, siloed datasets to gain visibility into threats, anomalies, and behaviors across the entire enterprise.

Ensure complete visibility, employ full-stack log analytics, and keep your mission-critical data safe and secure at all times. It’s a great way to boost your security posture and accelerate mean-time-to-response. You can also augment your SIEM with our solution and automate response with built-in alert correlation and custom STAR Rules.

The world’s largest and leading enterprises trust SentinelOne, including four of the Fortune 10 and hundreds of the Global 2000 giants. We have more in store and drive your business outcomes to the next level.

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

1. 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.

2. 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.

3. 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.

4. 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.