Enterprises are experiencing an explosion in the generation of data across different platforms. Utilizing this data means trying to leverage ingestion methods to have this data in a unified location. Making sure the ingestion is done in a timely way ensures that data insights can be derived to drive business growth.
The data may be structured, semistructured, or unstructured since it comes from diverse sources in batches or streams. This same data is being moved to the cloud or on-premise platforms through ETL or data ingestion tools. Integrating data from both cloud and on-premise environments allows an organization to know the holistic state of its business and take action where necessary.
This article is going to discuss real-time data ingestion, including what it is, why it’s important, and some of its benefits and limitations. Lastly, some real-time data ingestion tools will also be highlighted to show potential offerings that may fit your use case.
What is Real-Time Data Ingestion?
Data ingestion is defined as the process of aggregating data from one or many sources to be stored in a target system. The target system where the data is loaded could be a variety of types of storage. A few examples of common target systems are a database, data warehouse, data mart, or data lake. Once loaded, the data is usually used to perform ad-hoc queries, analytics, and other business operations.
Taking data ingestion a bit further, real-time data ingestion is the ability to move data from a source to a destination as soon as it is created or updated. In a real-time ingestion setup, data is continuously integrated across various sources, usually in small sizes, in real-time. stream processing is another term used to describe this process. With stream processing, data is not grouped in any way and is handled as a continuous stream. Each piece of data emitted or produced is transferred and loaded as soon as the ingestion layer recognizes the production of new data.
What Are The Types of Real-Time Data Ingestion?
There are two main ways of handling data ingestion, these types include batch processing and real-time data processing. With batch processing, the ingestion process collects data from one or many data sources incrementally. The batched data is then sent to a destination system or application where the data will be stored or used. With real-time data ingestion, every added or updated piece of data is individually loaded into the target system as soon as it is recognized by the ingestion layer. Streaming data is one example of a type of real-time data ingestion since it ensures the continuous flow of data from the source to the target system.
Data Ingestion vs. ETL
As already discussed, data ingestion is a broad term encompassing many different tactics for getting data from source to target systems. Any process that involves transferring raw data from a source to a target system could be referred to as data ingestion. The source in the data ingestion process could be anything ranging from databases, spreadsheets, IoT devices, third-party systems like CRMs, or even in-house applications. Destinations, also referred to as targets, generally tend to be a data warehouse, data lake, data mart, or similar platforms.
The general priority in data ingestion is focused on moving data from one place to another as quickly and efficiently as possible. This is typically done in a standard format for use by applications for operational purposes or prepared to meet a quality level for a specific type of storage.
Extract, Transform, and Load (ETL), compared to plain-old data ingestion, is a more specific process. ETL deals more with the preparation of data for a final storage location, like a data warehouse or data lake. It is used to retrieve and extract data, then transformed for storage where it can be used for Business Intelligence (BI), reporting, and analytics.
The Extract sub-process denotes the collection of data, Transform handles any necessary data transformations like sorting, filtering, or combining with another source, and Loading is focused on getting the data loaded into a destination.
Technically, the ETL process can be regarded as a branch or method of data ingestion. The ETL process is a pipeline that transforms raw data and standardizes it so that it can be moved from the source to a destination. It ensures that the data types, attributes, and properties of the raw data align with the required formats of the destination before loading it into a warehouse or data store.
Finally, it can be deduced from the explanations above that ETL and real-time data ingestion are both valuable tools for transferring data from various sources to a target system. ETL is typically used for processing data for reporting and analysis, while real-time data ingestion is focused on capturing and analyzing data in real time to enable faster decision-making.
Why Do We Need Real-Time Data Ingestion?
Implementing real-time data ingestion has become a technique that can not be overlooked by businesses today. The result of real-time data ingestion provides valuable insights to drive informed and necessary actions to move the business forward. Organizations can quickly identify and act on available growth opportunities and rectify issues immediately as they present themselves. This is all possible since real-time data ingestion provides constant data feeds to power analytics solutions and other tools. Real-time ingestion tools can provide these benefits without having an impact on the source systems with reduced latency in real or near real-time.
Traditional batch processing, usually seen in ETL and similar processes, requires data to be moved on a specific schedule. This usually plagues the data feed with delays or the possibility of delivering unwanted or obsolete data. With this approach, data may not be useful by the time it reaches its destination system. Real-time data ingestion, on the other hand, ensures the continuous flow of data in the needed time frame for immediate analysis by users and applications. It provides clear visibility, and access to current and comprehensive data so organizations can gain the maximum value from the data to make informed decisions.
Real-time data ingestion helps teams to be able to get the necessary data, from the necessary platforms to their destination platform of choice. Once implemented, this can give teams massive flexibility in how they move data around the enterprise. For business users, it means that certain use cases and insights that were unavailable without real-time data can now be unlocked.
Benefits of Real-Time Data Ingestion
To double down on the need for real-time ingestion let's dive into the array of benefits that can be associated with real-time ingestion. Below are a few highlighted benefits.
- Availability of data: Using real-time data ingestion will ensure that data is readily available for analysis and decision-making whenever you need it since it is always at your disposal as soon as it is captured or produced.
- Reduction of duplicate data: Real-time data ingestion helps in reducing the duplication of data as data is transferred immediately after there is an alteration or change. This is a benefit over data being sent in large, batched volumes where the mistake of sending data twice can occur.
- Ease of use: Most tools associated with real-time data ingestion are easy to use as they have a user-friendly interface eliminating the need for technical expertise. All you simply have to do is to select a data source and data destination.
- Reduction of load: Using real-time data can reduce the load on the source system as data will be ingested in tiny volumes instead of large batches, as seen with typical batch-ETL processes.
- Prompt decision-making: Businesses can use the analytics derived from data ingested in real-time to make valuable tactical decisions and also gain insights from the analysis to improve their applications to provide a better user experience.
- Fault-tolerant: Unlike batch processing, where you have to rerun the entire batch job if there is a failure, in real-time data ingestion, all you have to do is rerun feed from the point of failure in the data pipeline. The chances of missing data and data integrity issues are much more minimal.
Real-Time Data Ingestion Examples
There are many use cases of real-time data ingestion in the real world, below are a few that are commonly seen.
- Real-time data ingestion in monitoring systems in the hospital: Real-time data ingestion can be used in the hospital to keep track and record a patient’s vitals and alert staff in the case of an emergency. It can also be used to make predictions of a patient’s health status or condition.
- Real-time data ingestion in monitoring financial companies: Real-time data ingestion can help external data auditors to monitor transactions and identify potential issues. This can be done by spotting unusually high trading activity quickly, detecting suspicious trading activities by comparing it to previous patterns, getting an up-to-date view of all the data from a transaction, and receiving the precise timestamps and sequence of the trade.
- Customer relationship management: Real-time data can be used to improve customer relationships as the application can track a customer’s location, purchases made, and time of purchase. With this, you can offer a customer accessible locations or outlets close to them at favorable prices.
- Real-time data ingestion for analytics in the manufacturing of automotive: In the automotive industry, having real-time data is essential as this can be used to monitor a vehicle's condition in real-time, manage the manufacturing processes and quality of production, keep an inventory of available parts in the supply chain and identifying what needs to be ordered. All these scenarios lead to improvements that directly impact the customer experience.
Limitations of Real-Time Data Ingestion
- Increased complexity: With the ever-changing dynamics of data sources and internet devices, it can be challenging to perform data ingestion. It is difficult to connect to various data sources, identify and eliminate faults, clean the data, and pick out schema inconsistency in data.
- Sluggish process: Another challenge encountered in real-time data ingestion has to do with the writing of code to ingest data and manually creating mappings for the extraction, cleaning, and loading of data in real time. The volume of the data may be enormous and diverse making it difficult to write code. Data ingestion tools play a major role in overcoming this challenge.
- Unreliable data: If your connection is not done properly, you might not be getting the generated data in real-time hence, leading to unreliable analytics.
- High cost: Ingesting data in real-time can be difficult to implement as it requires high-performance hardware and can be quite expensive if not implemented in the efficient ways.
- Security: Securing your data is of utmost importance and you have to constantly be on your toes to fend off any cyber attacks trying to steal your sensitive data.
Best Tools for Real-Time Data Ingestion
Arcion is a data integration platform that enables businesses to collect, transform, and load data from various sources in real time. It provides a scalable and reliable infrastructure for real-time data ingestion and processing. Arcion is a no-code platform that is available as a fully-managed service through Arcion Cloud or as a self-managed, on-prem deployment through Arcion Self-Hosted.
Arcion uses multiple types of CDC. These include log-based, delta-based, and checksum-based CDC. The tool handles both DML and non-DML changes. It also supports schema evolution and conversion, transformation in columns, and DDLs.
As with a zero-code platform, you can set up and configure Arcion CDC using its intuitive UI in minutes - with no custom code required.
Arcion is the only CDC solution with an underlying end-to-end multi-threaded architecture, which supports auto-scaling both vertically and horizontally. Its patent-pending technology parallelizes every single Arcion CDC process for maximum throughput. So users get ultra-low latency and maximum throughput even as data volume grows.
100% Agentless Change Data Capture
Arcion CDC is a completely agentless CDC supporting more than 20 connectors. Arcion uses database logs, and does not read from the database itself. Being agentless, it eliminates inherent security concerns as well. It guarantees data replication at scale, in real-time.
Data Consistency Guaranteed
Arcion ensures zero data loss by adding extra validation support. It works automatically all along the replication and migration process, making it seamless and efficient.
Pre-Built Enterprise Data Connectors
See the full list of Arcion connectors. It is an extensive list of the most popular transactional databases, data warehouses, and cloud-native platforms. As an advantage over typical ETL tools, Arcion ensures that you have full control over data with the flexibility of automation. You can move data from a single source to multiple targets or vice versa.
Amazon Kinesis is a cloud-based real-time data streaming service available on AWS that enables businesses to collect, process, and analyze streaming data in real-time. Backed by the scalability of AWS, Kinesis can handle large amounts of data and allows for real-time data analytics and insights. Amazon Kinesis also makes it easy to share data between other AWS and third-party services easily.
Apache Kafka is an open-source distributed streaming platform that enables businesses to ingest and process large amounts of data in real time. It provides a highly scalable and fault-tolerant infrastructure for real-time data ingestion and processing. One of the most well-known solutions for streaming, Kafka is widely-supported and battle-tested.
Apache NiFi is an open-source data integration platform that enables businesses to collect, process, and distribute data from various sources in real time. It provides a drag-and-drop interface for building data flows and supports a wide range of data sources and destinations. NiFi's main strength is its ease of use and flexibility, making it ideal for small to medium-sized data processing and routing use cases.
Talend is a cloud-based data integration platform that enables the collection, transformation, and loading of data from various sources in real-time. It provides a scalable and reliable infrastructure for real-time data ingestion, processing, and analytics. It provides a suite of tools for data integration, data quality, and big data that enable businesses to extract insights and value from their data quickly.
Talend supports a wide range of data sources and destinations, including on-premise databases, cloud-based platforms, and big data technologies such as Hadoop and Spark. It also provides a visual interface for designing data integration workflows, making it easy for users to build, test, and deploy data integration solutions.
However, Talend’s open-source solution does not include tools for CDC, continuous integration, data security, versioning or data cataloging. Also, writing transformations in Talend is often labor-intensive.
Wavefront is a cloud-based analytics platform that has data ingestion capabilities. It provides a highly scalable and customizable infrastructure for real-time data analytics and insights. Wavefront's data ingestion capabilities are designed to handle massive volumes of data and are highly scalable, enabling businesses to ingest and analyze data from thousands of sources in real time. It provides a variety of ingestion methods, including APIs, agents, and integrations with various third-party services and tools.
Wavefront also provides real-time data processing capabilities, allowing businesses to analyze and correlate data as it is ingested. It can detect anomalies, alert on critical events, and provide deep insights into application and infrastructure performance.
Funnel is a cloud-based marketing data integration platform that enables businesses to collect, clean, and map data from various sources, such as advertising platforms, marketing automation tools, and CRM systems. It provides a unified view of marketing data, allowing businesses to make data-driven decisions and optimize their marketing campaigns. Funnel provides a UI for building data flows and supports a set of common data sources and destinations used within marketing functions.
In terms of data ingestion capabilities, Funnel provides a variety of methods for ingesting data from various sources. It has pre-built connectors for over 500 marketing platforms, including Facebook, Google, LinkedIn, and more. Funnel also supports custom data ingestion through APIs, CSV files, and FTP connections.
If you’re looking for database ingestion tools, Funnel might not be the best choice.
Improvado is another cloud-based marketing data integration platform. It provides a variety of methods for ingesting data from various sources. Improvado has over 200 pre-built connectors for popular marketing platforms like Google, Facebook, and LinkedIn. These connectors enable businesses to automate the data ingestion process and ensure data accuracy.
Improvado also supports custom data ingestion through APIs, CSV files, and FTP connections. All of this is available through a user-friendly UI for mapping data fields and transforming data into a standardized format, making it easy for businesses to integrate and normalize data from multiple sources.
Adverity is yet another cloud-based marketing data integration platform that enables businesses to collect, transform, and load data from various sources in real-time. Adverity includes a data modeling and transformation feature that enables users to manipulate data using custom formulas, join multiple data sources, create calculated metrics, and transform data into meaningful insights.
Adverity's data ingestion capabilities enable businesses to collect and unify data from various marketing sources, providing a comprehensive view of their marketing performance. It is a powerful platform for marketers and analysts who need to make data-driven decisions and optimize their marketing campaigns.
Airbyte is an open-source data integration platform that provides a modern and scalable solution for ingesting data from various sources into a data warehouse, data lake, or other destinations. It is a cloud-native platform that enables businesses to collect, clean, and transform data from different sources in real-time, without writing any code.
Airbyte provides a variety of methods for ingesting data from various sources and has pre-built connectors for over 200 sources, including databases, file systems, cloud applications, and APIs. There is also support for custom data ingestion through APIs, webhooks, and SDKs. It provides a user-friendly UI for mapping data fields and transforming data into a standardized format, making it easy for businesses to integrate and normalize data from multiple sources.
Airbyte also has a schema management feature that enables users to manage their data schemas centrally, ensuring consistency across different data sources. It also supports data transformations and enrichment, allowing businesses to clean and enrich their data before it is ingested into a target.
Though the OSS nature of Airbyte, you should do thorough research before considering adopt it for production. For example, Airbyte’s docs mentioned that their Postgres source can hardly handle database replication over 100GBs. Another issue mentioned by a Reddit user is that “the maintenance period on the connector is too long and/or the connector is fundamentally broken to begin with and/or the connector doesn’t exist at all”.
Dropbase is a cloud-based data integration platform that supports custom data ingestion through CSV, Excel, and Google Sheets files, providing users with the flexibility to import data from any source. It provides a UI for mapping data fields and transforming data into a standardized format, making it easy for users to integrate and normalize data from multiple sources.
Dropbase provides real-time data ingestion capabilities, allowing users to receive data updates in near real-time while providing data validation and error handling capabilities, ensuring that data is accurate and complete. It also has a data management feature that enables users to organize and manage their data in one place. This capability allows users to collaborate and share data with other team members securely.
Elastic Logstash is an open-source data processing and transformation tool that is part of the widely-used Elastic Stack. It allows users to ingest, transform, and load data from various sources into Elasticsearch or other destinations. Elastic Logstash provides a variety of methods for ingesting data from various sources including pre-built plugins for popular data sources, including databases, file systems, cloud applications, and APIs.
Elastic Logstash also supports custom data ingestion through its ability to read data from various input sources, including logs, messages, and events.
In conclusion, real-time data ingestion is a critical capability for businesses looking to gain insights and make decisions quickly based on the most up-to-date information. It enables businesses to capture and process data in real time, allowing them to respond to changing conditions and make informed decisions more quickly than traditional batch processing.
There are various tools and technologies available for real-time data ingestion, including Arcion, Amazon Kinesis, Apache Kafka, Apache Nifi, Talend, Elastic Logstash, and many others. Each of these tools provides different capabilities and features, so it's essential to choose the one that best meets your business and technical needs.
If you're looking for a reliable and scalable platform for real-time data ingestion, consider trying Arcion.
If you're ready to take your data ingestion capabilities to the next level, book a free demo with our team and give Arcion a try and see how it can help you harness the power of real-time data.