Top 12 Change Data Capture (CDC) Tools Simplifying Data 

80% of businesses that don’t currently have a cloud strategy are planning to adopt it in the next five years, and scalable data processing will be at the core of data transformation success. Slow and inaccurate data processing can hinder real-time visibility over operations, limit transaction speeds, and prevent organizations from making data-driven decisions. 

Besides processing speeds, data is continuously changing. So the question is, who can keep up? 60% of organizations are not confident in their data and analytics, while only 10% believe they do a good job of managing and analyzing data. In a world that relies and thrives on data, mastering it is a critical competitive advantage. 

Using CDC (Change Data Capture) tools is one of the best methods to address modern data’s complexities. CDC tools allow organizations to quickly identify the data changes and take the rapid action required to scale their businesses. 

This article will discuss what CDC is, why you need it, and which CDC tools you should consider to help drive your business forward. 

What is Change Data Capture?

Change data capture (CDC) is a process that enables organizations to automatically identify database changes. It provides real-time data movements by capturing and processing data continuously as soon as a database event occurs.

The key purpose of CDC is to ensure data accuracy to capture every data change as it happens. In the context of ETL (extract, transform, load) pipelines, which organizations widely use to transform data to target systems, CDC enables greater accuracy where older versions of ETL could only extract data bulks. Instead of a slow data processing pipeline, CDC integration eliminates the need for bulk updates, preventing system lags and downtime. 

Should you use CDC Tools?

Implementing a CDC process all by yourself is not an easy task, especially as it requires integration of multiple tools into your data architecture. That’s why it is always best to use a CDC tool instead of building a process of your own. 

There are many elements to consider, including equipping your development team with the knowledge and automation tools to monitor CDC. The best CDC tools are compatible with various data sources and data types. We’ll look at a few top-ranking options in the next section. 

Top 12 Change Data Capture tools

1. IBM Infosphere

The IBM Infosphere data integration platform enables data cleansing, transformation, and monitoring. It’s highly scalable and flexible, and its massively parallel processing (MPP) capabilities allow you to manage large volumes of data and scale accordingly.

Pros

  • Simple for both users and managers.
  • Straightforward integrations.
  • Performance is consistent and well-optimized.
  • Provides real-time data migrations that don’t compromise data quality.

Cons

  • You should be familiar with the app server aspects to maximise the platform.
  • No access to proper technotes on the IBM site in case of errors.
  • High price. 

2. Equalum

Equalum is a powerful log-based CDC tool with continuous data integration and real-time ETL streaming capabilities. It provides a fully managed, enterprise-grade CDC streaming solution that offers unlimited scale to increase data volumes, improve performance, and minimize system impact. To keep up with your digital transformation and cloud adoption journey, Equalum ingests data from on-premise to a cloud-based data warehouse for real-time visibility.

Pros

  • Provides an enterprise-class data ingestion platform for collecting, transforming, manipulating, and synchronizing data from multiple sources. 
  • Combines modern data transformation and manipulation with batch and streaming pipelines.
  • Create and deploy data pipelines in minutes.

3. Oracle GoldenGate

Oracle GoldenGate offers CDC and real-time delivery between heterogeneous systems to instantly replicate, transform, and filter transactional data from databases. It leverages CDC data replication from multiple sources to provide real-time analysis. In addition, it can replicate other sources, including Microsoft, IBM DB2, MongoDB, MySQL, and Spark.

Pros

  • Transfers data between databases to enable propagation in real-time.
  • Transforms data as it is transferred from tables in the same or separate databases.

Cons

  • On busy systems, online DDL updates might occasionally result in data mismatch for active-active replication.
  • High memory usage.
  • The system has numerous issues that prevent data dumping in XML or other formats like hdfs.

4. Precisely

Precisely is a leading data integrity company that provides a set of tools for data integrity management, including a CDC tool. It is used in over 100 countries and ensures maximum accuracy and consistency of your data.

Pros

  • Easy to integrate with mainframes and data lakes.
  • Can perform data mining tasks quickly.

Cons

  • Not suitable for data preparation.
  • GUI is not mature enough to establish connectivity with databases.

5. Keboola

Keebola is a cloud-based data platform that can distribute, modify, and integrate critical information quickly and simply. It’s a complete platform for data operations, with over 250 available connections to link data sources and destinations.

Pros

  • Competent data management, which improves analytical efficiency.
  • Customized data sourcing for accurate analysis.
  • Extended tools for business analysis.

Cons

  • Integration with Active Directory is difficult.
  • The pay-as-you-go system is quite expensive after the first five hours.

6. Fivetran

Fivetran is a fully automated data pipeline solution that centralizes data from any source and transports it to any warehouse. Fivetran mainly employs log-based replication and provides CDC as a feature. It can replicate databases, transfer data between on-premises systems and the cloud, and continuously monitor changes in data.

Pros

  • The data source connectors are high-quality and straightforward to use.
  • Running SQL scripts for integration and reports is simple for non-database users.
  • Big data ingestion is user-friendly and configuration-driven.

Cons

  • Data cannot be transformed before being sent to the destination.
  • Only a few destinations are supported as data warehouses.

7. Hevo Data

Hevo Data is a no-code data pipeline that can be used to load data into data warehouses. With Hevo, you can import various data sources, including relational databases, NoSQL databases, SaaS apps, files, and S3 buckets in real-time into any warehouse (e.g. Amazon Redshift, Google BigQuery, Snowflake). Hevo supports over 100 pre-built integrations, each of which is based on a native, niche source API.

Pros

  • Configuration and setup are simple.
  • Possibility of implementing ETL without coding knowledge.
  • Seamless data integration.

Cons

  • The changes from the raw connectors can sometimes be a little confusing and require a deep understanding of the source.
  • Writing transformation scripts is complicated.
  • Sometimes models take too much time to run.

8. Talend

Talend is an enterprise-class open-source data integration platform that incorporates CDC compatibility. As a company it provides multiple solutions, and Open Studio for data integration is its main product. It is free to use under an open-source license and offers three distinct models to support big data technologies.

Pros

  • Can move millions of records quickly and efficiently in a single task run.
  • Provides a good preview of the data counts moving between various systems.
  • Property files allow dynamically changing environmental variables easily.

Cons

  • Does not have the necessary components to perform ML-based deduplication and fuzzy matching.
  • Tier 1 applications are challenging to execute because of their low availability.
  • There are no effective methods for testing the components.

9. StreamSets

The DataOps platform from StreamSets has hybrid architecture and smart data pipelines with built-in data drift detection and handling. Along with collaboration and automation features, it supports the design-deploy-operate lifecycle. StreamSets continuously checks data in-flight to spot changes and predicts downstream difficulties.

Pros

  • Easy to use.
  • Many stages are readily available, including sources, processors, executors, and destinations.
  • Supports streaming and batch pipelines.
  • Key-vault integration for secrets fetching.

Cons

  • The logging process is complicated.
  • No option to create global variables.
  • Sometimes you have to keep information stored throughout the executions.

10. Striim

Striim provides a real-time data integration system that enables streaming analytics and continuous query processing. Striim integrates data from various sources, including events, log files, IoT sensor data, transaction/change data, and real-time correlation across several streams. In addition, the platform includes drag-and-drop dashboard builders and pre-built data pipelines.

Pros

  • Despite your data format, the platform gives you a place to prepare it.
  • When integrating with legacy data sources, you can choose from pre-built adapters and connectors or create your own.
  • Easy installations and upgrades.

Cons

  • The platform’s online interface and real-time dashboard are a little bit clunky.
  • High cost.

11. Qlik 

Qlik is a data-moving tool that provides organizations with immediate data insights. It offers features like data replication, replication, and streaming across numerous sources and targets.

Pros

  • Easy to combine data from numerous sources.
  • Highly scalable and flexible.
  • Log reading capability for multiple sources/targets

Cons

  • Initial setup and configuration can take some effort and time.
  • Requires users to write SQL queries or python code to process data.

12. Arcion

Arcion provides log-based CDC that supports many databases, data warehouses, and other popular technologies. It allows you to record DDLs, DMLs, schema changes, and several other non-DML changes. As a result, no-code CDC pipelines become reliable and straightforward to build and maintain.

Pros

  • Supports managed file transfers.
  • Supports data transformation, integration extraction, and analysis.
  • Metadata management.

Cons

  • No data filtering features.
  • Lack of data quality control.
  • No compliance tracking.

CDC: Simplifying Your Data and Your Decision-making

When choosing a CDC tool, you must ensure that it satisfies all your requirements and is scalable enough to keep up with your present and future business goals. Every tool has a different feature, and it could take some time to decide which one is right for you – but it’s worth the research to benefit from real-time analytics and data capture. 

Equalum is an excellent option for many organizations since it enables real-time streaming ETL for replication scenarios, analytics and BI tools, and application integration to maximize your decision-making flow. You can also find how Equalum can support your organization by booking a free demo.

The 6 Essentials for Real-Time Data Streaming Architecture

Harnessing robust cloud-based applications can help companies increase revenues by more than 30% yearly. To reach this pot of gold, 40% of businesses plan to pick up the pace of their cloud implementations and follow in the footsteps of popular apps like Uber, Netflix, and Lyft.

The only problem is that there are many hurdles and challenges to overcome before enjoying the benefits of a flexible and scalable cloud infrastructure. The first step in your cloud migration journey is to stream huge volumes of data from existing sources to the cloud. Without the right tools and technologies, data streaming can be time-consuming and costly for your engineers.  

To make migration happen successfully, your data streaming architecture needs to work hard to provide cloud transitions as fast as possible and continually manage a high volume of data.  

What is real-time data streaming?

Real-time data streaming is the constant flow of data produced by multiple sources. It enables you to collect, analyze, and deliver data streams as they generate in real-time. Examples of streaming data include log files produced by users of mobile applications, e-commerce transactions, and telemetry from cloud-based devices.

There are two ways to stream data: batch and real-time. Real-time streaming data is continuously generated, enabling you to use the information for concurrent analysis exactly when you ingest it. Batch processing differs because it receives the data in batches and stores the source until enough data has been collected according to specific parameters. It comes in the form of unending streams of occurrences. This data comes in all sizes, formats, and locations, including on-premise, in the cloud, and a hybrid cloud environment.  

What is data streaming architecture?

Data streaming architecture is a framework of software components that consume and process significant amounts of streaming data from many sources. A streaming data architecture ingests data instantly when you create it, continues it to storage, and could include tools for real-time processing, data manipulation, and predictive analysis. 

Data streams create vast amounts of data, which is primarily semi-structured and needs a lot of pre-processing to be effective and useful. A data streaming architecture contains several components:

Source: There could be tens of thousands of machines or software programs, otherwise called sources, that rapidly and continuously produce large amounts of data. 

Ingestion: Ingestion enables you to capture continuously produced data from thousands of devices reliably and safely.

Storage: Depending on your scale, latency, and processing demands, you can choose a service that will satisfy your storage needs.

Processing: Some processing services require only a few clicks to modify and transport data, allowing you to integrate ML into sophisticated, unique real-time applications.

Analysis: Transmit streaming data to various completely integrated data storage, data warehouses, and analytics services for additional analysis or long-term storage.

What are the use cases of data streaming?

Today’s businesses can’t always rely on batch data processing because it doesn’t allow the visibility they need to monitor data in motion. Data streaming architecture has use cases in almost every sector, from analytics to data science and application integration. This technology is advantageous to every sector that uses big data and can profit from continuous, real-time insights. Business use cases include:

  • Business analytics and performance monitoring
  • Real-time sales campaign analytics
  • Fraud detection
  • Customer behavioral analytics
  • Supply chain and shipping
Real-Time Data Streaming Architecture Equalum

What are the benefits of real-time data streaming?

As long as you can scale with the amount of raw data generated, you can acquire valuable insights on data in transit and use historical data or batch data that has been stored. Here are three main use cases of data streaming:

1. Movement of Real-Time Data

As well as examining data as it is ingested, you can store it for further evaluations by data streams from tens of thousands of endpoints and execute ETL operations on massive quantities of continuous, high-speed data in real-time. 

2. Processing of Event Streams

The most popular use cases involve change data capture (CDC) and communication between a large number of independent microservices for real-time recording, threat monitoring, and event response. 

3. Data Evaluation

Evaluate data as soon as it is generated and allow real-time decisions to improve customer experiences, avoid networking problems, or update your organization in real-time on important business KPIs.

The 6 Essentials for Real-Time Data Streaming Architecture

A flexible streaming architecture simplifies the complexity of conventional data processing architecture into a single self-service product that can convert event streams into data warehouses that are available for analytics. Furthermore, it makes it simpler to keep up with innovation and outperform the competition. Here are the essentials that the best data streaming architecture contains.

1. Scalability 

Thanks to the rise of cloud-based technologies, data streaming architecture is thrust into the spotlight. It needs to be scalable to keep up with increased data volumes, compliance standards, and shifting company needs as businesses adopt cloud tech.

Scalability is especially important when a system malfunctions. The pace of the log data from each source may go from a few KB to MB, maybe even GB. The quantity of raw data proliferates as additional capacity, resources, and servers are added while programs scale. Hence the need for a scalable data streaming architecture. 

2. Fault Tolerance

Fault tolerance is the ability to carry on as normal after a malfunction and enable swift recovery. Your architecture needs advanced systems that transparently recover if a failure occurs. The system’s state must be preserved to ensure no data is lost. 

There are checklists you can follow to improve the fault tolerance of your data streaming architecture, such as preventing a single failure point by using data from various sources and in different forms. You can also maintain high availability and endurance while storing streams of data.

3. Real-Time ETL Tools

Process streaming data is a crucial part of big data architecture in companies with large data volumes. Real-time analytics is made possible by a variety of managed service frameworks that build an end-to-end streaming data pipeline in the cloud. In-memory stream processing has significantly advanced streaming ETL. When you have large datasets that need preprocessing before ingestion into your real-time analytics database, it’s the best option.

For example, Equalum enables real-time, in-memory streaming ETL for replication scenarios, analytics, and BI tools for real-time decision-making. 

4. Storage Options

Real-time data streaming solutions are built to facilitate distributed processing and reduce consumer and producer dependency. Deployment too tightly coupled to one central cluster can choke the autonomy of projects and domains. As a result, the adoption of streaming services and data usage will be limited. Containerization promotes more flexibility and domain independence in a distributed cloud deployment architecture. 

5. Analytics Capabilities 

A streaming data analytics database is made explicitly for analytics, which requires it to quickly prepare enormous data streams for queries after ingestion. Even complex query results should return rapidly. Additionally, the number of simultaneous requests must be scalable without causing conflict that hinders your ingest. 

For enhanced efficiency, your database should isolate the query processing from the ingest and employ SQL. Even better is a real-time analytics database that can execute rollups, searches, aggregations, joins, and other SQL actions as the data is consumed.

6. Change Data Capture (CDC) Tools

You can continually capture changes made in your operational database (like MongoDB). The problem is that data warehouses are immutable, making it difficult to modify the data and maintain real-time synchronization between the operational database and the data warehouse. This even happens with some of the most well-known cloud data warehouses. To solve this, you can use Equalum. Our solution enables you to continuously access real-time data, track changes, and apply transformations before ETL using built-in CDC capabilities.

High-Speed Data Delivery Becomes a Reality With Equalum

The world revolves around real-time data streaming, which is why reviewing your architecture is more important than ever. Choosing the right components will set your business up for future success by ensuring you can scale up and be flexible as needed. Whether you are planning to migrate to the cloud, harness real-time insights for business KPIs or another use case, data streaming can help you achieve your goals. 

Equalum steps in to support businesses on their cloud migration or adoption journey by enabling continuous access to real-time data using built-in CDC capabilities and streaming ETL. With Equalum’s help, better visibility and fast data delivery can be a reality. Want to know how it works? Book a demo today

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