In the more modern ELT pipeline Extract, Load, Transform , data is loaded immediately and then transformed in the target system, typically a cloud-based data warehouse , data lake , or data lakehouse. ELT operates either on a micro-batch timescale, only loading the data modified since the last successful load, or CDC timescale which continually loads data as it changes at the source. This phase refers to the process of placing the data into the target system, where it can be analyzed by BI or analytics tools.
Learn how to modernize your data and analytics environment with scalable, efficient and real-time data replication that does not impact production systems. There are many use cases for CDC in your overall data integration strategy. You may be moving data into a data warehouse or data lake or creating an operational data store or a replica of the source data in real-time. Ultimately, CDC will help your organization obtain greater value from your data by allowing you to integrate and analyze data faster—and use fewer system resources in the process.
Here are some key benefits:. There are a few ways to implement a change data capture system. Before built-in features were introduced for CDC in Oracle, SQL Server, and a few other databases, developers and DBAs utilized techniques such as table differencing, change-value selection, and database triggers to capture changes made to a database.
These methods, however, can be inefficient or intrusive and tend to place substantial overhead on source servers. These features utilize a background process to scan database transaction logs in order to capture changed data. Therefore, transactions are unaffected, and the performance impact on source servers is minimized.
The most popular method is to use a transaction log which records changes made to the database data and metadata. Here we discuss the three primary approaches. Log-based CDC. This is the most efficient way to implement CDC. When a new transaction comes into a database, it gets logged into a log file with no impact on the source system.
And you can pick up those changes and then move those changes from the log. Few database vendors provide embedded CDC or change data capture technology. And, even when they do, the technology is generally not suitable for capturing data changes from other types of source systems.
This means IT teams must learn, configure, and monitor separate CDC tools for each type of database system in use at their organization. The newest generation of log-based CDC tools are fully integrated.
They can also replicate data to targets such as Snowflake and Azure. This lets you leverage one tool for all of your real-time data integration and data warehousing needs. This quick video describes how end-to-end data integration platforms streamline, automate, and rapidly accelerate your entire information pipeline from raw to ready data.
In a broader sense, CDC can be classified into 2 categories:. At a high level, there are several techniques and technologies for handling the Change Data Capture processes CDC process.
The top 4 change data capture implementation techniques are:. This technique depends on a timestamp field in the source to identify and extract the changed data sets.
This technique requires the creation of database triggers to identify the changes that have occurred in the source system and then capture those changes into the target database. The implementation of this technique is specific to the database on which the triggers need to be created. This technique involves creating a complete extract of data from the source table in the target staging area.
Therefore, the next time the incremental data needs to be loaded, a second version or snapshot of the source table is compared to the original one for spotting the changes.
Almost all Database Management Systems have a transaction log file that records all changes and modifications in the database made by each transaction.
This log-based technique depends on this log information to spot the changes and perform CDC operations. In an ETL process, the first step is the extraction of data from various source systems and storing the extracted data in staging tables. Just as the name implies, ETL tools extract data from a source, transform the data while on transit, then load the data into the target storage of your choice. CDC delivers change data to a data pipeline tool either in batch or real-time.
This approach drastically improves the efficiency of the entire data transfer process. It reduces the associated costs including computing, storage, network bandwidth, and human resources.
These movements of data can be scheduled on a regular basis or triggered to occur. Raw data is extracted from an array of sources and sometimes placed in a Data Lake. This data could be formatted in:.
The transformation stage is where you apply any business rules and regulations to achieve. At this point, you might be wondering which is the better option, hand-coding the CDC infrastructure for ETL or investing in a tool that can handle this out of the box?
Therefore, the opportunities become endless when you empower yourself and your team with the right data pipeline platform. Moreover, the blog discussed the relationship between the CDC and ETL process and also listed the various use cases of both of them. Also, it mentioned the limitations that you will face if you will perform the CDC process manually using the ETL method.
Using Hevo Data for your data pipelines allows you to complete integration jobs a lot faster than hand-coding, that too at a fraction of the cost. You could replicate the entire source database. However, this method is not as efficient, since you replicate data that has already been replicated in the past.
Detecting which table rows have been changed added, deleted, altered , and replicating those changes makes the entire replication process orders of magnitude more efficient. In modern data environments, where the volume of data keeps growing, CDC is the only viable data replication technique that scales with your data operations. Dive deeper into how CDC achieves the multiple benefits for data operations with our in-depth guide. One question remains unanswered, though. Of course, you could build a CDC solution in-house.
But there are several shortcomings to the homegrown approach:. Instead of diluting your limited engineering resources further, rely on a tool to do the heavy lifting for you.
Keboola is an end-to-end data operation platform offering out-of-the-box features for a variety of data ops:. Discover all Keboola has to offer with its always free tier. Yes, that is correct, Keboola does not offer just a free trial, it offers an always-free account for all your data needs.
Primarily it is designed to replicate Oracle Database with optimized high-speed data movement. Alongside data replication, Oracle GoldenGate is also used for end-to-end monitoring of stream data processing solutions without the need to allocate or manage compute environments.
Qlik Replicate, formerly known as Attunity Replicate, is a data ingestion, replication, and streaming tool. Qlik Replicate uses parallel threading to process Big Data loads, making it a viable candidate for Big Data analytics and integrations.
Its main purpose is to integrate data from a multitude of sources into your data warehouse. Talend offers connections and replications across a myriad of data source types within its easy-to-use interface. Though Talend Data Integration is extremely powerful as a CDC tool, it lacks version control as one of the features and it is definitely geared more towards huge enterprises. Join our newsletter noSpamWePromise.
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