What Is Data Ingestion? A Complete Guide

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 on a daily basis.

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. The next section will look at the types of data ingestion that are available.

Types of data ingestion

There are three types of data ingestion methods that can be used for the ingestion of your data. When choosing the method you will use, the choice will be greatly influenced by your type of business, the goals to be achieved, your IT infrastructure, your timeline, and your budget. Let’s take a look at each type in a bit more detail:

Batch Processing: Batch processing is the most common type of ingestion. In this type, the ingestion layer collects and groups data from various sources incrementally and transfers the data in batches to a location, application, or system where it is needed. This transfer is based on existing schedules, activation of certain conditions through trigger events, or any logical ordering you might have set in place to ensure that the data is sent. This type of ingestion is useful for companies that need to collect specific data daily with activities that demand daily report generation or attendance sheets, for example. This approach is usually less expensive than others and is considered a legacy approach in some cases.
Real-time data ingestion: This type of data ingestion, also referred to as stream processing, involves the collection and sending of data from the source system in real-time to the destination. In stream processing, there is no grouping; rather, data is sourced, processed, and loaded as soon as the ingestion layer recognizes the new data. One of the most common solutions used to implement this type of ingestion is by using Change Data Capture (CDC). This type of ingestion incurs more cost and is expensive compared to batch ingestion. This is because the system has to constantly monitor the sources for any change to make sure it is reflected in the target platform. Despite the cost, it is very helpful for companies that run analytics that require refreshed data for making real-time operational decisions. For example, real-time data ingestion is helpful for stock market trading decisions, and infrastructure monitoring (such as sewage or electricity grids).
Lambda-based data ingestion: This type of data ingestion is a combination of the two types listed previously. Batch processing is employed to gather the data in groups and real-time processing is used to offer a different view of time-sensitive data. It divides data into groups but ingests them in smaller increments making it suitable for applications that require streaming data.

Benefits of data ingestion

Some of the benefits of using data ingestion are:

Availability: With data ingestion in an organization, data is made available and accessible to all users. Since data is gathered from multiple sources and moved to a unified storage location, anyone with rights to the company’s data can easily gain access to the data they require for analysis.
Uniformity: Having a good data ingestion process improves the quality of the data by turning different data types into unified data types. This allows for easier understanding and manipulation of the data for better analytics in the data warehouse.
Increased productivity: Data ingestion helps businesses use the data to be more productive. Data engineering teams can become more flexible and develop the agility to scale data since it can easily be moved to any system of choice.
Save time and money: Data ingestion saves your organization time and money by collecting data from multiple sources through one approach used to gather data, therefore, increasing efficiency. Data analysts, data scientists, and others can easily connect to the data by building data pipelines with minimal cost.
Improved decision-making: Real-time data ingestion allows businesses to make informed and better decisions. Opportunities are more easily uncovered with the flow of data into a single platform. With the analytics derived from the ingested data, tactical decisions are easier to make and track against potential targets and KPIs.

Data ingestion tools and features

There are many data ingestion tools available on the market and the variety grows each day. The products themselves are defined as software products that collect and transfer data from a source to a target destination. The data can be structured, semi-structured, or unstructured and is moved through data ingestion pipelines from one point to another.

Data ingestion tools have different features to consider as you decide on which to use. A brief overview of a few of these features is highlighted below:

Extraction: Data ingestion tools are used to collate data from a variety of sources and replicate it to another platform, such as a data warehouse. It is important to choose data ingestion tools that can extract data from applications, databases, and other technologies and platforms that you are using.
Size/Volume: Determine the volume of data an ingestion tool can handle before choosing to implement it. Data ingestion tools can be adjusted to handle a large workload and big volume of data depending on your needs. Each individual platform may have a different way of scaling to accommodate this. You’ll also want to anticipate future data volumes since it is likely over time they will increase.
Data types/Format: Select tools that can handle different data types, whether structured, semi-structured, or unstructured raw data. Structured data has its formats in tabular form, unstructured data has to do with data such as videos, images, sounds, and semi-structured data contains formats such as JSON files, CSS files, and so on. Choosing a tool that can handle your specific data formats is crucial in making sure implementation goes smoothly.
Frequency of ingestion/Processing: This has to do with how often data will be ingested and processed. It can be in real-time or in batches therefore, going for an ingestion tool should be based on your business needs. Some tools may even be able to accommodate both types of processing.
Tracking of data flow and visualization: Data ingestion tools can enable users to keep track of the ingestion process by offering visualization of the flow of data within the system. Although not necessary, visualizations and a good UI can make using the platform much more simple. Some platforms may even offer built-in error log viewing that may be more convenient than digging through physical logs.
Security and privacy: Security features are paramount in data ingestion tools. They come in a variety of formats such as SSL, HTTP over SSL, encryption, and others. Depending on the industry or type of data you will be ingesting, be sure to pick the tools that meet the standard of security and privacy compliance required.

What are the challenges of data ingestion and big data sets?

Data ingestion can experience the following challenges:

Scalability: It can become difficult to scale large data sets when ingesting data. This is because the amount of data being processed could require horizontal or vertical scaling of the underlying infrastructure to accommodate the increased load. Performance can take a steep decline when scaling, although not always.
Data quality: Ensuring data quality during data ingestion can be a major challenge because it is difficult to maintain and monitor. Checking data quality should be a part of the ingestion process to always have an accurate representation of the data.
Diverse ecosystem: There is an ever-growing number of data types and sources, making it difficult for data engineering teams to build a sound-proof ingestion framework. Some tools only support a subset of technologies, causing organizations to also use multiple tools requiring multiple skill sets.
Costs: Overall ingestion costs increase as businesses and data volumes grow. The need to ingest all the data will lead to having more storage systems and servers thereby raising the cost of ingestion.
Security: Data can be exposed during the ingestion process as data is staged at different points in the ingestion pipeline. These exposures make the data ingestion process vulnerable to security breaches and leaked data. Outside of the platform chosen, data teams have to fend off constant cyber attacks that may arise in order not to expose sensitive data.

Data ingestion vs ETL

Data ingestion, as mentioned earlier, is the process in which data is sourced and collected for use or storage. The data is collected from one or more data sources and transformed into the correct format of the target system if required. Finally, the data is then written to the applications that require it or to destinations that will store the data.

On the other hand, ETL, which stands for Extract, Transform, and Load, is the process of preparing data to be stored and used by a platform such as a data warehouse or data lake. It is used to extract and retrieve data for storage that can be used for data analytics, reporting, and business intelligence. The aim of the entire process is to ensure that the data is delivered in a format that meets the requirements of the target platform.

The main focus of data ingestion is to get data into any system, whether it be for storage or an application, that needs the data in a given format or for operational use. The focus of ETL is to transform data into well-defined structures optimized for analytics before storing it in the destination. Therefore, data ingestion can be deemed as a broad term for getting data into required formats, structure, and quality, whereas ETL is used for the transformation of data in conjunction with a data warehouse or data lake.

Data ingestion architecture

The architecture of a data ingestion process helps define the flow of data. The architecture itself consists of the following layers:

Data ingestion layer: This layer is where data is collected from different sources.
Data collector layer: The second layer of the ingestion architecture collects data from the ingestion layer and determines if and how it should be transferred to other layers of the ingestion pipeline. It also breaks the data down for further analytical processing.
Data processing layer: The data passed from the previous layer is processed for transfer to the next layer, which is storage. It determines the destination where the data will be sent and also classifies the data.
Data storage layer: This layer determines the most efficient data storage location for the processed data.
Data query layer: This is the analytical point in the data ingestion architecture. This is the layer where data can be queried and valuable insights can be extracted.
Data visualization layer: This is the final layer of the architecture which deals with the presentation of data. It does this by displaying data in a visual and understandable format for users to gain further insights.

Data Ingestion with Example

Data ingestion can take a wide variety of forms which include the following:

Taking data from different in-house systems into a data lake, data warehouse, or a standardized repository for reporting or analytics.
Ingesting a constant stream of data from multiple sources to optimize and get the most out of a marketing campaign.
Enabling customers to ingest data through an API to a cloud-based analytics platform.

There are many other use cases for data ingestion tools. But, before using them, it’s important to have a few guiding principles outlined. Below are a few to guide you.

Make sure you identify your business outcomes and desires. You can do this by stating what you want to achieve with the data ingestion process you want to implement. This includes knowing where your data resides, how many data sources you currently have, and how often newly extracted data will be needed by the system or process requiring it.
Design your ingestion architecture from the information you gathered from the previous step. Each implementation will be highly custom, so knowing if the data source is a SaaS platform, files, or databases will inform you of the tools that are best suited for you. You’ll also want to determine the performance requirements and the cost parameters of the system you will build.
Once you have carried out the considerations above, proceed to carry out the technical details behind implementing the data ingestion process. Here you will leverage your team, including engineers, to identify the objective and strategy and validate that the selected tools match your needs.
Finally, implement the data ingestion and transformation process to meet your previously defined technical and business objectives.

Using Arcion for data ingestion

Data ingestion can be done through multiple tools or handled within a single one. Arcion is a single tool which can aid with many parts of the data ingestion process. Arcion is an extremely flexible solution that can make implementing a data ingestion process easy and efficient.

By using Arcion, it’s easy for organizations to implement a real time data ingestion pipeline using many different data sources. Easily move data from a source database to an external system, such as a big data platform like Snowflake or Databricks, with no code required. For many use cases, Arcion is much easier and more flexible than the built-in data ingestion tools supported by the major database providers.

Here are a few quick highlights of why choose Arcion for your data ingestion needs:

No-code connector support for 20+ sources and target databases and data warehouses. Arcion also supports multiple deployment types, across cloud and on-premise installations.
Arcion’s agentless CDC ensures there is zero impact on the source data systems. Arcion reads directly from database logs, never reading from the production database. No need to install a software process on the production system.
Automatic schema conversion & schema evolution support out-of-the-box (including SQL and NoSQL conversion)
Patent-pending distributed & highly scalable architecture: Arcion is the only end-to-end multi-threaded CDC solution on the market that auto-scales vertically & horizontally. Any process that Arcion runs on Source & Target is parallelized using patent-pending techniques to achieve maximum throughput.
Built-in high availability (HA): Arcion is designed with high availability built-in. It makes the pipeline robust without disruption and data is always available in the target, in real-time.
Auto-recovery (patent-pending): Internally, Arcion does a lot of check-pointing. Therefore, any time the process gets killed for any reason (e.g., database, disk, network, server crashes), it resumes from the point where it was left off, instead of restarting from scratch. The entire process is highly optimized with a novel design that makes the recovery extremely fast.

Conclusion

In this article, we covered many facets of data ingestion including what it is and how to go about implementing it. 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 own organization leverage it to its own competitive advantage.

If you’re looking for an enterprise-grade data ingestion solution, with real-time CDC capability, download Arcion Self-hosted for free. We don’t ask for any payment info and you can access the full features.

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