Data ingestion refers to moving data from data sources to the destination site for further analysis and processing. This data comes from several sources, such as on-prem databases, data lakes, SaaS apps, and IoT devices, and end up in several target units like data marts and cloud data warehouses.
A data ingestion platform is a critical technology that helps companies use their vast amounts of data. To help your company make more informed decisions, dive into this technology. We will discuss real-time data ingestion, benefits, challenges, and capabilities of data ingestion, and much more.
Types of Data Ingestion
The three standard data ingestion methods include real-time, batches, or a combination of both, called lambda architecture. Businesses can choose one of these types based on their IT infrastructure, business objectives, and financial restrictions.
- Real-time data ingestion: The process of gathering and moving data from source systems in real-time through platforms like change data capture (CDC) is known as real-time data ingestion. This platform consistently helps to monitor transactions or redo logs and moves changed data without interfering with the database workload.
Real-time data ingestion is crucial for time-sensitive use cases like power grid monitoring or stock market trading when companies must quickly respond to new data. Real-time data pipelines are significant when forming quick operational decisions and identifying and acting upon new insights.
- Batch-based data ingestion refers to the process of gathering and moving data in batches at scheduled intervals. The ingestion layer helps to collect data depending on simple schedules, logical ordering, and any main trigger events. This type of data ingestion is helpful when organizations need to gather data points daily or don’t need data for real-time decision-making.
- Lambda architecture-based data ingestion: Lambda data ingestion architecture is a setup that includes both real-time and batch techniques. It includes batch, serving, and speed layers. Data is selected by the former layers in batches, while the latter collects data that has not yet been picked up by serving and slower batch layers. This existing hand-off among various layers ensures that data is accessible for querying with low latency.
Advantages of Data Ingestion
The data ingestion process provides several facilities, allowing teams to handle data more efficiently and stay ahead of the market. Some expected data ingestion benefits include:
- Data is less complex: Modern data ingestion pipelines, integrated with ETL tools, help to transform several types of data into predefined formats and then provide it to a data warehouse.
- Can create better software tools and apps: Engineers use data ingestion platforms to support their apps and software tools. It helps move data quickly and provides a better user experience.
- Help to make informed decisions: Real-time data ingestion enables companies to notice issues and opportunities and make better decisions rapidly.
- Data is readily available: Data ingestion enables organizations to gather data stored across several sites and move it to a unified database for prompt access and analysis.
- Employees save time and money: By automating data ingestion, engineers can now focus on other more important tasks.
What are the Common Data Ingestion Challenges?
Building and maintaining a data ingestion pipeline got easier than ever, but it still has several challenges:
- The diversity of the data landscape is expanding. Teams must manage various data types and sources, posing challenges in developing a robust future data ingestion framework.
- Navigating complex legal obligations. Data teams must familiarize themselves with various data privacy and protection regulations, such as GDPR, HIPAA, and SOC 2, to ensure compliance with the law.
- Increasing cyber-security concerns. It is increasingly difficult to protect valuable and sensitive data from cyber threats as malicious actors persist in attacking them.
Exploring the Right Solution: Significant Data Ingestion Capabilities
Data ingestion is a significant capability for any modern data architecture. An appropriate data ingestion architecture enables you to ingest any data quickly. It includes application, database, file, and streaming with detailed and high-performance connectivity for real-time and batch processing data ingestion. Below are the core attributes of any data ingestion tool:
- Unified experience for data ingestion: Organizational data is distributed across several entities. Hence, we need a unified source to ingest data from these entities.
You should look for an ingestion solution that can apply simple transformations on the data at the edge. It is done before it is ingested into the lake.
- Capability to manage schema drift and unstructured data: Numerous sources emit unstructured data, which emphasizes the importance of parsing it to reveal and understand its structure. The ever-changing source data structure, known as schema drift, poses a significant obstacle for many organizations.
Look for a solution that intelligently handles schema drift and automatically synchronizes changes with the target systems.
- Versatile out-of-the-box connectivity: The unified data ingestion software connects several sources. This includes mainframes, databases, files, applications, IoT, and other streaming sources.
It should be able to persist the enriched data into several cloud data warehouses, data lakes, and messaging units.
- High performance: An effective data ingestion pipeline allows continuous availability and enables actions like data cleansing and timestamping. This is done during the ingestion process without interruptions. A Kappa architecture can achieve real-time data ingestion, while a Lambda architecture can handle batch processing.
Moreover, selecting a data ingestion solution that ensures recovery from job failures, offers high availability, and guarantees precisely one delivery for replication scenarios is advisable.
- Wizard-derived data ingestion: You can ingest data efficiently using a wizard-derived tool without hand coding. The data must go into a cloud data warehouse through the CDC feature. This ensures you have the most consistent and current data for analytics.
- Real-time data ingestion: Enhancing the ingestion of real-time log, clickstream data, and CDC into Amazon Kinesis or Microsoft Axure Event Hub is crucial. It allows real-time analytics.
- Cost-efficient: Well-thought-out data ingestion must save your company money by automating currently time-consuming and costly processes. Additionally, data ingestion is cheaper when your organization needs to pay for skilled technical resources or infrastructure to support it.
Revolutionizing Cloud Modernization: The Power of Secure Data Ingestion
Achieving higher business profits from cloud adoption means implementing new features consistently and rapidly as per regulatory, business, and architectural guidelines over time.
Secure data ingestion- a significant element of an enterprise data platform- offers a staging section in the cloud. This isolates raw data such that its owner(s) can utilize cloud data platform services and tools to prepare the data for release into the general data platform environment.
This means you can run analytics and share data. It will be performed securely and consistently, respecting rules, regulations, and budget guidelines.
Creating a Base for Digital Transformation
Private, confidential, or sensitive data is subjected to data handling principles as a part of information security practices. This principle is frequently violated by uploading raw data before the appropriate handling rules have been applied.
Isolating raw data which arrives on the platform enables its custodian and managers to process it before releasing it for direct access. By segregating data and processing, data managers have entire control over access to their data- even from platform administrators and developers.
Data classification, cleansing, masking, and tagging mechanisms must remain segregated from business data transformation to meet data handling needs. This domain performs field-level changes like type casting to ensure that published data can be interoperable with other platform sources.
Secure data ingestion helps in satisfying data handling regulations. It also sets the stage for business changes like the application of Google Analytics- a cloud-native analytics platform depending on Google BigQuery. It offers the capability to scale while ensuring robust security governance and controls so that you can turn your data into insights.
Data ingestion is a crucial technology that helps organizations extract and transfer data in an automated manner. With data ingestion pipelines installed, IT and other business units can emphasize extracting value from data and finding new insights. Also, automated data ingestion can also work as a critical transformer in a competitive environment.