5 Reasons Why You Should Automate Data Ingestion

You need to use data for different enterprise operations, including sales, CRM, marketing, and supply chain management, to gain a competitive edge and increase profitability. Over the years, data production has increased significantly. According to a Statista report, the global data creation will grow by more than 394 zettabytes by 2028. You need a robust roadmap to manage such massive amounts of data.

While working with high-volume data, having a good data ingestion strategy is essential to avoid data loss and reduce latency. Automation can play a crucial role in achieving this. By leveraging automated data ingestion, you can collect, store, and use data effectively to make well-informed business decisions.

Let’s understand what automated data ingestion is and why you should opt for it for efficient data management and downstream enterprise operations.

What is Automated Data Ingestion?

Automated data ingestion is the process of using automation tools and techniques to collect, process, and store data from various sources into a suitable data storage system. Unlike manual procedure, automated data ingestion involves the use of solutions that require minimal to no human intervention. By using such tools, you can improve the speed and efficiency of data ingestion and overall data-based workflows in your organization.

Data ingestion is a preliminary step for data integration. However, the data ingestion vs. data integration processes differ from each other. Data ingestion involves collecting raw data, while the process of transforming and consolidating this data is known as data integration. Automating data ingestion can facilitate faster data integration, ensuring data availability for subsequent tasks like data analysis. 

5 Reasons Why You Should Automate Data Ingestion

Some of the reasons why you should adopt an automated data ingestion process:

1. Improves Data Accuracy

Manual data ingestion is prone to human errors, such as typos or misconfigured data mappings, which can compromise data integrity. Automated solutions enable you to eliminate these inaccuracies as they include built-in error handling and validation mechanisms that allow you to detect and resolve inconsistencies quickly. Due to this, you can prevent incorrect data from entering your workflow, ensuring data integrity.

2. Enhance Data Accessibility 

You can reduce the latency in collecting and processing data with the help of automated data ingestion platforms, ensuring data is available when required. Cloud-based data ingestion tools facilitate data extraction from several sources. If your business relies on real-time analytics, you can also opt for streaming data ingestion solutions that allow you to continuously retrieve data from sources. Such refined data accessibility is critical for real-time applications, including fraud detection, IoT monitoring, or weather forecasting.

3. Achieve Scalability

Manual data ingestion cannot keep up with the growing data requirements. Automated platforms facilitate batch ingestion, which allows you to extract large volumes of data in chunks effectively. Additionally, streaming data ingestion tools like Apache Kafka, which has a distributed data ingestion architecture, enable you to manage high-scale data. Such automation solutions allow you to build scalable data pipelines that can handle high data velocity and volume. 

4. Optimize Costs

Most of the automated data ingestion tools are cloud-hosted and fully managed. Using such platforms, you can save expenditure on infrastructure maintenance. Implementing automation also reduces operational failures, saving the resources required to reperform error-prone processes.

5. Increase Employee Productivity

Utilizing automated data ingestion tools, you and your team can save the time required to collect, transfer, and store data. The time saved can be invested in other important activities, such as research and innovation, driving business growth.

How to Automate Data Ingestion?

Data ingestion automation ensures accuracy and data integrity. To automate your data ingestion workflow, you can follow the below steps:

Identify Data Sources

Evaluate the sources from which you want to ingest data. The sources can include databases, flat files, APIs, or IoT devices.

Choose the Right Data Ingestion Approach

You can opt for batch, real-time, or hybrid data ingestion approach depending on your data volume, latency requirements, and availability of resources. Choosing the right data ingestion technique can help you to enhance data accessibility for downstream operations.

Select Automated Data Ingestion Tools

Next, you must select an appropriate automated data ingestion tool according to your requirements. Airbyte, Apache Kafka, Apache Nifi, Amazon Kinesis, and Apache Flume are some of the prominently used data ingestion services.

Implement Data Transformation and Validation

After extracting data using the data ingestion tool, you can standardize the data using data cleaning and transformation techniques. This may include deduplicating, removing null values, or aggregating data to streamline it for further analysis. Some platforms provide AI and ML capabilities to validate data by checking missing fields and formatting inconsistencies.

Set up Data Destination

Once you ingest and transform data, you can store it in a suitable destination data system. Data warehouses, data lakes, databases, and data streaming platforms are some of the highly used destination solutions.

Ensure Data Security

While building data ingestion pipelines, you should deploy robust security mechanisms to protect sensitive data. Data encryption, role-based access control, and multi-step authentication are some of the methods that you can use to maintain data security. When selecting a data ingestion tool, you should check whether it complies with major data regulatory frameworks like GDPR and HIPAA.

3 Tools to Automate Data Ingestion

There are several platforms that can help you automate data ingestion, and three such prominent tools are:

1. Airbyte

Airbyte is a powerful data movement platform that you can use to ingest data from multiple sources. It offers an extensive library of 550+ pre-built connectors, which you can use to extract data from any source and load it to a destination of your choice. If the connector that you want to use is not present, you can build it yourself. For this, Airbyte provides Connector Builder, Low Code Connector Development Kit (CDK), Python CDK, and Java CDK.

While configuring connectors, you can utilize Airbyte’s change data capture (CDC) feature. It enables you to incrementally capture changes made to the source data system and replicate them in the destination to maintain data consistency. If the data that you have extracted is semi-structured or unstructured, you can directly load such data in vector databases supported by Airbyte. This includes vector store destinations like Pinecone, Milvus, Weaviate, and Chroma.

After loading data to a suitable destination, you can integrate Airbyte with dbt, a command line interface tool, to transform the data into a required form.

Here are some additional features of Airbyte:

• AI-powered Connector Builder: While developing your connector using Connector Builder, you can leverage an AI assistant. It can pre-fill necessary configuration fields to minimize setup time. The AI assistant also offers intelligent solutions to fine-tune the connector configuration process.‍
• Developer-friendly Pipeline: PyAirbyte, an open-source Python library, offers a set of utilities for using Airbyte connectors in the Python ecosystem. With the help of PyAirbyte, you can extract data from several sources and load it to SQL caches like Postgres. You may then transform this data using Python libraries like Pandas to convert it into a standardized format for business analytics.‍
• Multiple Data Sync Modes: Airbyte provides different sync modes to replicate data from source to destination. These data sync modes are incremental append, incremental append + deduped, full refresh append, full refresh overwrite, and full refresh overwrite + deduped.

2. Apache Kafka

Apache Kafka is an open-source event streaming platform that allows you to publish(write), subscribe (read), process, and store streams of data. Kafka’s architecture consists of servers and clients that interact with each other through TCP network protocol. The servers include multiple data centers and cloud regions, and the servers forming the storage layer are called brokers. Conversely, clients are applications that you can use to read, write, and process streams of events.

The client applications that you can use to publish events are called producers, while the applications that subscribe to these events are called consumers. The events can be organized effectively in a folder-like system called topics.

Some prominent features of Kafka include:

• Scalability: You can leverage Kafka’s distributed architecture to accommodate high volumes of data across multiple nodes by adding extra brokers. The partitioning method also helps you to divide Kafka topics into multiple partitions to process large amounts of data.‍
• Kafka Connect: Unlike Airbyte, Apache Kafka does not offer pre-built connectors to build data pipelines. However, you can use Kafka Connect, a framework that enables you to exchange data between Kafka and other data systems.

3. Apache NiFi

Apache NiFi is an open-source data movement platform that helps you manage dataflow, the flow of information across different data systems. You can design, control, and monitor dataflows using NiFi’s simple web-based user interface. The data records that you want to ingest in NiFi are known as Flowfiles, and you can ingest them through batch or stream processing.

Some additional features of Apache NiFi:

• Reliable Data Flow: To ensure guaranteed data delivery, NiFi supports the write-ahead log method and content repository feature for local data storage. Using these capabilities, you can perform frequent high transactions, copy-on-write, and read/write disk operations.‍
• Data Buffering: NiFi facilitates data buffering and storage of queued data temporarily before it is completely ingested. When the queues hit a specific limit, you can use the back pressure feature to prevent excessive data load.

Conclusion

Data ingestion automation is critical for the successful management of high-volume data. This blog gives you a comprehensive overview of automated data ingestion and five reasons why you should automate your data ingestion procedure. To properly ingest data, you need to select the right data ingestion tool out of the several available platforms. Once you choose an appropriate solution, you can complete the data ingestion process to use the data later for various real-world applications.