Allows you to implement streaming ETL, change data capture (CDC), and process large files and real-time APIs. You can connect and realize event-driven architectures with distributed streaming systems such as Kafka, Amazon SQS, and more.
JoinAPI Data Lake CDC RDMS
The JoinAPI Data Lake focuses on CDC – Change Data Capture services to identify and capture data changes. It’s essential for organizations and environments where data is rapidly changing and it’s important to keep track of changes in real-time.
JoinAPI Data Lake CDC can be used in a variety of applications, including real-time data analysis, event processing, and data replication between heterogeneous systems. This approach is valuable for organizations needing quick and efficient access to ever-evolving data for analysis and decision-making.
Advantages of using JoinAPI Data Lake CDC:
By utilizing Change Data Capture (CDC) technique, only data changes are captured and stored, significantly reducing the amount of data to be processed and stored. This results in storage and processing resource savings.
Since JoinAPI Data Lake CDC captures data changes as they occur in source systems, the Data Lake is updated in real-time. This allows organizations to have access to the latest information for timely analysis and decision-making.
With access to real-time updated data, analytical teams can quickly respond to changes and trends in the data. This enables more agile and accurate decision-making.
As only data changes are captured, there’s greater assurance of data consistency and integrity in the Data Lake. This reduces the risk of inconsistencies or discrepancies in data used for analysis and reporting.
JoinAPI Data Lake CDC can capture and integrate data from heterogeneous systems such as relational databases, file systems, messaging systems, among others. This allows organizations to unify and analyze data from diverse sources in one place.
Since only data changes are captured and processed, the time required to replicate and update data in the Data Lake is reduced, resulting in lower latency time between the occurrence of changes in source systems and the availability of these changes for analysis.
Change Identification
Change Identification functionality is a crucial step in the Change Data Capture (CDC) process. In this step, the system identifies which data has changed since the last capture point, in order to capture only relevant changes for update or analysis. Here’s a detailed explanation of how this functionality operates:
Change identification begins by comparing the current state of the data with a previous reference point. This reference point could be the last time changes were captured or a specific version of the data.
Once the current state of the data and the previous reference point are established, the system analyzes the differences between them to identify the changes. This may involve line-by-line comparisons in a database, checking timestamps, or other techniques depending on the system context.
The identified changes can be of three main types: inserts (newly added records), updates (existing records modified), and deletes (removed records). The system needs to identify each type of change accurately and efficiently.
In many systems, especially in databases, timestamps or transaction logs are used to record data changes. These timestamps or logs are essential for determining when a change occurred and what nature that change was.
In environments where multiple operations are happening concurrently on the data, it’s necessary to consider concurrency when identifying changes. This ensures that all changes are accurately captured and not lost or overwritten by other ongoing operations.
In case failures occur during the change identification process, the system should be able to reconstruct the current state of the data from the last valid reference point. This ensures the integrity and consistency of the captured data.
For environments with large volumes of data and high change speeds, change identification needs to be efficient and scalable. This means the system should be able to handle large volumes of data and identify changes quickly, ensuring adequate performance.
Change Logging
Change Logging functionality is an essential step in the Change Data Capture (CDC) process. After identifying changes in the data, these alterations need to be logged in some form so they can be processed, stored, or replicated as needed. Here’s a detailed explanation of how this functionality operates:
After identifying changes in the data, the next step is to capture these changes accurately and efficiently. This may involve creating records or data entries that represent each identified change.
The captured changes are logged in a specific format depending on system requirements and the type of data involved. This may include formats such as JSON, XML, CSV, database records, or any other structured format.
To ensure the integrity and traceability of the changes, it’s common to include metadata along with each change record. This metadata may include information such as the type of change (insertion, update, deletion), the timestamp of the change, the user or process responsible for the change, among others.
In transactional environments where multiple operations may be occurring simultaneously on the data, it’s important to ensure transaction control when logging changes. This ensures data consistency and prevents integrity issues.
Depending on system requirements and the nature of data changes, logging can occur in real-time as changes are identified or in batches, where multiple changes are logged at once at regular intervals.
In environments where multiple processes are logging changes in the data simultaneously, concurrency must be managed, and conflicts resolved to ensure all changes are logged correctly and without data loss.
Once logged, changes need to be persisted securely and durable. This typically involves storing them in a database, file system, or other reliable storage medium.
In addition to logging changes in the data, it’s important to monitor and audit the logging process to ensure all changes are captured properly and there are no integrity or compliance issues.
Change Data Capture (CDC)
The Change Data Capture (CDC) Capture functionality is a critical part of the Change Data Capture process, allowing for the identification and recording of data changes as they occur in database systems or other data sources. Here’s a detailed explanation of how this functionality operates:
Change capture involves a continuous process of monitoring data sources for any changes. This can be done through various techniques such as accessing database transaction logs, using triggers, or periodically querying the data.
When a change is detected in the data source, such as an insertion, update, or deletion of records, a change event is triggered. This event indicates that a data modification operation has occurred and needs to be captured.
After identifying the change event, relevant information about that change is recorded. This may include details of the affected record, such as primary keys, old and new values, timestamps, and other pertinent metadata.
In some cases, it may be necessary to apply filters to determine which changes are relevant for capture. This can be done based on criteria such as operation type, specific data values, or other user-defined conditions.
To ensure efficiency and performance, change capture is designed to be as fast and lightweight as possible. This may involve techniques such as minimizing processing overhead, query optimization, or using low-latency capture mechanisms.
Change capture must ensure that the captured data is synchronized consistently with the original data source. This is essential to maintain data integrity and avoid inconsistency issues.
The change capture functionality should be able to handle a variety of data sources, including relational databases, file systems, cloud services, and other heterogeneous data sources.
For environments with large data volumes or high rates of change, change capture must be scalable and able to handle peak loads efficiently. Additionally, it’s important for the system to be fault-tolerant to ensure continuity of operations in case of issues.
Data Replication
Data Replication functionality is a key step in many data systems, including those employing the Change Data Capture (CDC) technique. Data replication involves copying and distributing data from a source to one or more destinations, ensuring that changes made at the source are reflected in the destinations in real-time or near real-time. Here’s a detailed explanation of how this functionality operates:
The data replication process typically begins with identifying changes at the data source. This can be done using CDC techniques such as transaction log monitoring, real-time change capture, or periodic scans of the data.
Once changes at the source are identified, these changes are captured and recorded in some form. This may include creating change records or logs containing information about the operations performed on the data.
The changes captured at the source are then transported to the replication destinations. This may involve transferring data over the network using storage queues and topics.
Upon arrival at the replication destinations, changes captured at the source are applied to the data at the corresponding destinations. This ensures that the data at the destinations is always up-to-date and synchronized with the original data source.
During the replication process, it’s crucial to ensure data consistency and integrity. This often involves using distributed transactions and concurrency control mechanisms to ensure that all changes are applied correctly and without conflicts.
One-to-many unidirectional.
In addition to data replication itself, it’s important to monitor and manage the replication process to ensure its effectiveness and reliability. This may involve monitoring performance, detecting and resolving failures, and adjusting configuration as needed.
Data replication must also consider data security and privacy issues. This may include encrypting data during transfer and controlling access to data at replication destinations to ensure that only authorized users have access.
Delivery of Captured Data
The Delivery of Captured Data functionality is a crucial step in the Change Data Capture (CDC) process, which involves providing the captured data to appropriate destinations for analysis, further processing, or storage. Here’s a detailed explanation of how this functionality operates:
Before being delivered to final destinations, captured data may undergo additional processing such as data transformation, cleansing, enrichment, or aggregation. This can be done to prepare the data for analysis or integration with other systems.
After processing, captured data is routed to appropriate destinations based on predefined routing rules. These destinations may include data storage systems, data warehouses, data lakes, real-time analytics systems, business applications, or other target systems.
Delivery of captured data can occur in real-time as changes are captured and processed, or in batches where data is delivered at scheduled intervals. The delivery method depends on system requirements and the intended use of the data.
During the delivery process, it’s crucial to ensure the integrity and consistency of the data. This may involve verifying that all captured data has been successfully delivered, detecting and handling delivery errors or failures, and reconciling data between sources and destinations.
In environments where multiple operations are occurring simultaneously on the data, managing concurrency and resolving conflicts during data delivery is necessary. This ensures that all data changes are delivered correctly and without loss.
In addition to ensuring the delivery of captured data, it’s also important to monitor and audit the delivery process to ensure its effectiveness and reliability. This may include performance monitoring, detecting and resolving delivery issues, and logging activities for auditing purposes.
The delivery of captured data must also consider data security and privacy issues. This may include encrypting data during transfer, controlling access to data at delivery destinations, and complying with data protection regulations.
What is CDC?
The Delivery of Captured Data functionality is a crucial step in the Change Data Capture (CDC) process, which involves providing the captured data to appropriate destinations for analysis, further processing, or storage. Here’s a detailed explanation of how this functionality operates:
The CDC process begins by identifying the changes that have occurred in the data since the last capture point. This is done by comparing the current state of the data with a previous reference point.
Once identified, data changes are recorded in some form. This can be done through triggers in databases, transaction logs, or other system-specific mechanisms.
The identified changes are then captured and recorded in a designated location. These captured data can be processed immediately or stored for later analysis.
In some cases, captured changes may be replicated to other systems or databases to maintain data consistency across different environments.
Once captured, data changes can be delivered to applications or systems requiring real-time or near-real-time access to updated information.
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