What is Cloud Data Ingestion: A Comprehensive Guide

Data ingestion is an integral part of modern enterprise workflows, as it helps you collect and unify data from various sources to enhance business operations. However, due to growing data volumes, on-premise data storage solutions may be highly maintenance-intensive and less scalable, leading to latency issues and increased expenditures. To overcome these challenges, you can use a fast and more reliable cloud data ingestion approach.

Let’s understand what cloud data ingestion is, along with its key features and some prominent cloud-hosted data ingestion tools. This will aid you in ensuring uninterrupted data movement in your organizational workflows.

What is Cloud Data Ingestion? Does it Differ from Standard Data Ingestion?

Cloud data ingestion is the process of extracting raw data from various sources and transferring it to a cloud-based storage system or data warehouse. Unlike traditional on-premise data ingestion, which relies on local databases, data warehouses, or a storage system, cloud-based ingestion eliminates the need for physical infrastructure. 

On-premise data ingestion setup requires investment in software licensing and hardware, which can be expensive and require ongoing maintenance. In addition to this, it has limited scalability due to resource constraints. In contrast, cloud ingestion provides on-demand scalability, cost efficiency, and painless maintenance, making it a preferred choice for data-driven organizations.

Key Features of Cloud Data Ingestion

Adopting cloud data ingestion can significantly improve the productivity of the data collection process. Here are some important features of cloud data ingestion:

Better Data Accessibility

This approach enables you to access data from anywhere and anytime. With this enhanced accessibility, you and your team can work collaboratively without infrastructural constraints. Once ingested, the data is readily available for business intelligence, analytics, and decision-making. 

Scalability

With cloud data ingestion, you can dynamically scale the computational, storage, and processing power according to the change in workloads. Whether dealing with small or big datasets on a petabyte scale, this capability allows you to handle workloads without human intervention.

Automation

Cloud data ingestion empowers you to automate data extraction and loading processes through pre-built connectors, event-driven triggers, and scheduling features. You can also utilize AI and machine learning technology to optimize data collection, preparation, and transfer to destinations of your choice. 

Cost Efficiency

By opting for cloud data ingestion, you can eliminate the cost of maintaining additional infrastructural setups as cloud providers handle these aspects. Most cloud platforms support a pay-as-you-go pricing model. In this pricing structure, you only need to pay for the services that you use. 

What Makes Cloud Data Ingestion Important in Today's Day & Age?

With growing data production, it has become imperative to find ways to manage and utilize this massive amount of data. At an organizational level, one of the best solutions can be to integrate all your datasets.

According to a Grand View Research report, the data integration market size will expand at a CAGR of 11.7% till 2030. The prominent reasons behind such tremendous growth of the data integration market include increasing dependence of business operations on data and demand for real-time services.

Data integration involves the extraction and consolidation of data from multiple sources into a centralized destination. To better this process, you can opt for cloud data integration, in which cloud ingestion is an integral step.

Such an approach facilitates proper management of growing data volume and real-time generation of analytical insights for faster decision-making.

How to Setup Cloud Data Ingestion with Airbyte: A Step-By-Step Guide

To perform cloud data ingestion, you can opt for an effective tool like Airbyte. It is a data movement platform that provides you with a vast library of no-code 550+ pre-built connectors. You can use these connectors to extract data from diverse sources and load it into a cloud-based destination of your choice. If the connectors that you want to use are not available, you can build them on your own using Connector Builder, Low Code Connector Development Kit (CDK), Python CDK, and Java CDK.

While building custom connectors using Connector Builder, you can utilize its AI assistant. It enables you to automatically pre-fill necessary configuration fields and provides intelligent suggestions to fine-tune the connector configuration process.

Let’s build a data ingestion pipeline to migrate data from MySQL to a cloud-based system like BigQuery. This will help you understand how you can utilize Airbyte to achieve cloud data ingestion.

Step 1: Set up MySQL Source Connector

• Log in or sign up for Airbyte Cloud if you do not already have an account.

• From the left navigation pane on the main dashboard, click Sources. You will be redirected to Set up a New Source page. Here, enter MySQL in the search box.

• On the Create a Source page, enter the necessary details, including Host Name, Port, User Name, and Password.

• In the Encryption section, choose an appropriate encryption method from the drop-down list on the right.

• You can initiate an SSH tunnel by selecting one of the following two options, SSH Key Authentication or Password Authentication.

• Finally, click Set up Source.

Step 2: Set up Google BigQuery Destination Connector

After the source connector is configured, you can set up the BigQuery destination connector using the below steps:

• Click Destinations from the left navigation pane on Airbyte’s main dashboard.
• On the Set Up a New Destination page, enter Google BigQuery in the search box.

• You will be directed to the Create a Destination page. Here, enter the GCP Project ID for the project containing your BigQuery dataset in the Project ID option.

• In the Dataset Location field, specify the location of your dataset. However, you cannot change this location later. Next, enter your BigQuery dataset ID in the Dataset ID option.
• You can define the way in which you want to load data to BigQuery tables in the Loading Method section. There are two approaches to this: Batch Standard Inserts and GCS Staging. 

• Lastly, enter the Google Cloud Service Account Key in JSON format in the Service Account Key JSON field.
• Finally, click the Set Up Destination button.

Step 3: Set up Connection

To finish setting up your connection, proceed with the following steps:

• Select Connections. Next, choose MySQL as the source and BigQuery as the destination.
• Select the Data Sync Mode using which you want to replicate your data. The available data sync modes are Incremental Append, Incremental Append + Deduped, Full Refresh Append, Full Refresh Overwrite, and Full Refresh Overwrite + Deduped.
• Next, select the streams you want to replicate. In Airbyte, streams refer to groups of related data records. Click Next.
• On the Configure Connection page, fill in the necessary fields, including Schedule Type and Replication Frequency. Click Finish & Sync.
• Lastly, you will be redirected to the Connection Overview page. Here, you will see tabs such as Status, Timeline, Schema, Transformation, and Settings. You can use these tabs to track the performance of your connection.

This concludes the process of setting up a cloud data ingestion pipeline from MySQL to BigQuery using Airbyte. With its pre-built connectors and intuitive UI, Airbyte allows you to streamline the extraction and transfer of data between disparate cloud-based storage solutions.

To further enhance workflow, you can integrate Airbyte with data orchestration tools such as Apache Airflow, Dagster, Prefect, and Kestra. This enables proper scheduling, monitoring, and execution of your data pipelines.

Let’s look at some additional features of Airbyte:

• Build Developer-Friendly Pipelines: PyAirbyte is an open-source Python library that offers a set of utilities for using Airbyte connectors in the Python ecosystem. Using PyAiryte, you can extract data from several sources and load it to SQL caches such as Postgres or Snowflake. ‍
• Streamline GenAI Workflows: You can directly load semi-structured or unstructured data into vector store destinations while using Airbyte. It supports popular vector databases such as Pinecone, Weaviate, Milvus, and Chroma. By integrating these vector databases with LLMs, you can conduct better contextual searches, streamlining your GenAI workflows.‍
• Using Terraform to Build Automated Data Pipeline: With Terraform, you can configure Airbyte resources like connections, sources, and destinations. It enables you to define and provision infrastructure through code.‍
• Change Data Capture (CDC): Airbyte’s CDC feature allows you to incrementally capture changes made to the source data system and replicate them into the destination data system. Through this, you can keep your source and destination in sync with each other, ensuring data consistency.‍
• Custom Transformation Using dbt: After building a data ingestion pipeline, you can integrate Airbyte with dbt, a command line tool, to transform raw data into a standard format. Such enriched and transformed data is useful for high-quality analytics and reporting.‍
• Flexible Pricing: Airbyte offers three paid versions: Cloud, Teams, and Enterprise. The Cloud version has a volume-based pricing model, while Teams and Enterprise editions have capacity-based pricing. To know more about Airbyte’s paid editions, you can refer to the pricing documentation.

5 Cloud Data Ingestion Tools Worth Looking Into

Other than Airbyte, there are several cloud data ingestion tools that you can use to build a cloud data ingestion pipeline. Some of these are discussed below:

1. Google Dataflow

Google Dataflow is a fully managed cloud data ingestion solution that you can use to build robust data pipelines. It assists you in performing both batch and stream data ingestion. Apache Beam SDK, an open-source programming model, is a prominent component of Google Dataflow. This model enables you to process data in parallel by distributing the data load across multiple virtual machines.

Some well-known features of Google Dataflow are:

• Flexible Data Pipeline Development: Dataflow offers multiple options to help you build data pipelines. The first approach involves writing code using Apache Beam SDKs. If you find this complex, you can use Dataflow templates to execute pipelines quickly. Alternatively, you can use JupyterLab Notebook to develop and run reusable pipelines.‍
• Visual Monitoring: To ensure the proper functioning of your data pipelines, you can utilize the Dataflow monitoring interface in the Google Cloud console. It provides a graphical representation of various stages of your data pipeline to showcase progress and issues in the pipeline.

2. AWS Glue

AWS Glue is a fully managed data integration service that allows you to ingest data from more than 70 data sources. You can transform and consolidate this data in a suitable destination, such as an S3 bucket or Redshift. By retrieving data from these cloud target systems, you can conduct powerful analytics.

Here are some additional features of AWS Glue:

• Robust Schema Discovery: For accurate data ingestion, AWS Glue offers a feature called Glue crawlers, which enables you to connect with your source or destination. With the help of crawlers, you can classify data into groups to determine source and destination schemas. Using this information, you can create and store metadata in a centralized repository called AWS Glue Data Catalog for effective ETL integration.‍
• Job Scheduling System: While using Glue, you can execute ETL jobs automatically as new data arrives in your source data systems. You can do this by setting up event-based triggers or job execution schedules. Owing to such capabilities, you can utilize AWS Glue for real-time data ingestion.

3. Apache Kafka

Apache Kafka is a real-time event streaming platform that allows you to ingest data from a variety of sources, including databases, sensors, and social media platforms. To utilize Kafka for cloud data ingestion, you can deploy Kafka on cloud vendors such as Confluent Cloud or AWS.

Here are some additional features of Kafka:

• Distributed Architecture: With distributed data ingestion architecture, Kafka consists of servers and clients that interact with each other via TCP network protocol. The group of servers can span across multiple cloud regions and form the basis of Kafka’s functionality. Some servers form a storage layer called brokers.‍
• Pub/Sub Model: Kafka follows the pub/sub model as it facilitates asynchronous communication between its components, called producers and consumers. Producers are the client applications that allow you to publish (write) events to a broker. Conversely, applications that help you to subscribe (read) to these events are known as consumers.

4. Azure Data Factory

Azure Data Factory (ADF) is a cloud-based data integration service managed by Microsoft. It allows you to design and orchestrate data ingestion pipelines with the help of a variety of pre-built connectors. With ADF, you can set custom event triggers to automate the pipeline execution. This ensures quick data movement to suitable cloud-based destinations.

Let’s look at some additional features of ADF:

• Data Compression: When transferring data using ADF’s Copy activity, you can apply different data compression methods like GZip. This reduces storage costs and speeds up data transfers. ‍
• Data Validation Capabilities: During data pipeline development, ADF offers data preview and validation services that help you monitor if data is retrieved correctly from the source. This enables you to create high-quality pipelines with good data consistency.

5. Matillion

Matillion is a cloud data integration tool that helps you build data pipelines for business analytics and reporting. It offers a large library of pre-built connectors to ingest data from diverse sources and load it to a suitable destination. To speed up data-driven workflows, you can leverage Matillion Copilot, an AI assistant that facilitates data pipeline development using simple prompts.

Some notable features of Matillion include:

• Support for Diverse Data Types: Using Matillion source connectors, you can ingest structured, semi-structured, and unstructured data and load it to any cloud destination. Such versatility allows you to use Matillion to develop data pipelines for AI applications.‍
• Strong Data Security: You can conduct cloud data ingestion securely while using Matillion, as it provides strong security features. Audit logging, multi-factor authentication, and role-based access control are a few of the several mechanisms.

Conclusion

Cloud data ingestion is critical for collecting and storing data for various business operations. This blog gives you a detailed overview of cloud data ingestion along with its important features. 

To perform cloud data ingestion, you can opt for any of the several available data ingestion tools like Airbyte, Matillion, or Apache Kafka. These tools support several cloud-based destinations, such as BigQuery and Redshift, to enable you to store the extracted data. You can then utilize this data for diverse operations in finance, manufacturing, and e-commerce sectors.