--- title: RheoData Blog | batch processing description: batch processing | RheoData Blog Posts --- [![RheoData - Logo - transparent-1](https://rheodata.com/hs-fs/hubfs/RheoData%20-%20Logo%20-%20transparent-1.png?width=250&height=50&name=RheoData%20-%20Logo%20-%20transparent-1.png "RheoData - Logo - transparent-1")](https://rheodata.com/) - Who We Are - [About Us](https://rheodata.com/who-we-are) - Services - Consulting - [The Studio](https://rheodata.com/the-studio) - Accelerators - [FrostCore](https://rheodata.com/frostcore) - [FrostAI](https://rheodata.com/frostai) - [RedCore](https://rheodata.com/redcore) - [RedAI](https://rheodata.com/redai) - [RedGuard](https://rheodata.com/redguard) - [BlueCore](https://rheodata.com/bluecore) - [Calypso](https://rheodata.com/calypso) - Services - [Data Integration](https://rheodata.com/data-integration) - [Analytics](https://rheodata.com/data-analytics) - [Multi-Cloud](https://rheodata.com/multi-cloud) - [Oracle@Google Cloud](https://rheodata.com/oracle-google-cloud) - 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[Shoe Carnival](https://rheodata.com/customer-stories/shoe-carnival-goldengate-microservices-migration) - [Icon](https://rheodata.com/customer-stories/icon-transatlantic-replication) - [Inovalon](https://rheodata.com/customer-stories/inovalon-data-pipeline-automation) - Resources - [Blog](https://rheodata.com/en-us/blog) - Books - [Pro Oracle GoldenGate 23ai](https://rheodata.com/pro-oracle-goldengate-23ai-for-the-dba-pdf-landing-page) - [Contact](https://rheodata.com/contact) Posts about # batch processing ## [Modern Data Platform with Oracle Cloud Infrastructure (OCI)](https://rheodata.com/en-us/blog/moder-data-platform) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:41 PM Does data drive your organization? Have you started your transition to a Modern Data Platform? Does... [CONTINUE READING](https://rheodata.com/en-us/blog/moder-data-platform) ## [Data Pipelines – What is a data pipeline?](https://rheodata.com/en-us/blog/data-pipelines) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:37 PM A data pipeline is a method in which raw data or unchanged data is ingested from various data... [CONTINUE READING](https://rheodata.com/en-us/blog/data-pipelines) ## [Different pipelines for the masses](https://rheodata.com/en-us/blog/different-pipelines-masses) Posted by [Bobby Curtis](https://rheodata.com/en-us/blog/author/bobby-curtis) | Nov 10, 2025 9:29:02 PM If you work in the IT industry long enough, you start to see messaging or wording of items to... 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[vector database consolidation (1)](https://rheodata.com/en-us/blog/tag/vector-database-consolidation) See all ##### About RheoData RheoData is based out of Metro Atlanta, GA and provide expert Oracle, Microsoft, Google, and Snowflake services.  Let us  know how we can help! ##### Links - [About Us](https://rheodata.com/who-we-are) - [FrostCore](https://rheodata.com/frostcore) - [RedCore](https://rheodata.com/redcore) - [BlueCore](https://rheodata.com/bluecore) ##### Contact us [hello@rheodata.com](mailto:hello@rheodata.com) ©RheoData2026. All Rights Reserved. ```json { "@context" : "https://schema.org", "@type" : "Organization", "address" : { "@type" : "PostalAddress", "addressCountry" : "US", "addressRegion" : "GA" }, "description" : "Elite specialist partner for Oracle, Snowflake, BigQuery, and AI-ready data platforms.", "email" : "hello@rheodata.com", "foundingDate" : "2020", "logo" : "https://rheodata.com/hubfs/RheoData%20-%20Logo%20-%20transparent-1.png", "name" : "RheoData", "sameAs" : [ "https://www.linkedin.com/company/rheodata" ], "url" : "https://rheodata.com" } ``` ```json { "@context" : "https://schema.org", "@type" : "WebSite", "name" : "RheoData", "potentialAction" : { "@type" : "SearchAction", "query-input" : "required name=search_term_string", "target" : "https://rheodata.com/search?q={search_term_string}" }, "url" : "https://rheodata.com" } ``` ```json { "@context" : "https://schema.org", "@type" : "BlogPosting", "author" : { "@type" : "Person", "name" : "", "url" : "" }, "dateModified" : "2026-04-17T17:31:04+0000", "datePublished" : "2025-10-30T17:41:53+0000", "description" : "batch processing | RheoData Blog Posts", "headline" : "<span id="hs_cos_wrapper_name" class="hs_cos_wrapper hs_cos_wrapper_meta_field hs_cos_wrapper_type_text" style="" data-hs-cos-general-type="meta_field" data-hs-cos-type="text" >rheodata_blog listing page</span>", "image" : "", "mainEntityOfPage" : { "@id" : "https://rheodata.com/en-us/blog/tag/batch-processing", "@type" : "WebPage" }, "publisher" : { "@type" : "Organization", "logo" : { "@type" : "ImageObject", "url" : "https://rheodata.com/hubfs/RheoData%20-%20Logo%20-%20transparent-1.png" }, "name" : "RheoData" } } ``` ```json { "@context" : "http://schema.org", "@type" : "BlogPosting", "articleBody" : "Does data drive your organization? Have you started your transition to a Modern Data Platform? Does your organization understand what it means or takes to transition to a Modern Data Platform? Yes, these are questions that organizations think they know the answer to until they start the transition and bills from cloud providers come in. All concerns that RheoData can help organizations solve from Day 1 and help maintain on Day 2 through our service offerings. At the same time, a solid understanding of what a data drive platform consists of is key. Without expertise in Oracle Cloud Infrastructure (OCI) and data integration, we find it essential for organizations to understand and define their goals related to Modern Data Platforms. For the better part of a decade, the cloud has brought about many changes that organizations have to evaluate, review, and use data daily. One of these changes is acknowledging that the modern data architecture’s idea of one-size-fits-all eventually leads to compromises. It is not simply about integrating a data lake with a data warehouse, but rather a data lake, data warehouse, and purpose-built data stores, enabling a unified governance approach and simplified data movement. In approaching the modern data architecture on OCI, Oracle – the leader in databases with Oracle Database and MySQL (open-source), customers can rapidly build scalable data lakes, use a broad and deep collection of purpose-built data services, ensure compliance via a unified data access, security, and governance, scale their systems at a low cost without compromising performance, and easily share data across organizational boundaries, allowing them to make decisions with speed and agility at scale. Why you need a modern data platform Data volumes are increasing alarmingly and are projected to reach or surpass 180 zettabytes by 2025 (less than two years away). Traditional on-premises data ingestion and analytics approaches can’t handle these data volumes because they don’t scale well enough and are too expensive to build and maintain. Many organizations are taking all their data from various silos and aggregating all that data in one location, what many call a data lake, to do analytics and ML directly on top of that data. The same organizations are storing or offloading other data into purpose-built data stores to analyze and generate insights from structured and unstructured data, leaving organizations with data distributed across the enterprise and possibly unavailable to the broader organization. Through a Modern Data Platform, data ingestion and data analytics are governed, secure, and performance-driven to provide meaningful business insight, real-time visibility, and accurate forecasting or predictions. Why choose Oracle’s Modern Data Platform Oracle offers the most complete, open, and intelligent modern data platform. These three pillars of Oracle’s modern data platform make a compelling argument for Oracle Cloud Infrastructure (OCI) and why organizations should leverage their existing investments in Oracle technologies or take another look at the only cloud built for enterprises: Complete: Oracle’s Modern Data Platform is designed with you in mind, offering, and providing a unique suite of services for the whole data stack Open: Empowers organizations with flexible workload deployment, seamless integration, and open solutions. Meeting you where you are. Intelligent: Benefits from the latest artificial intelligence innovations to surface insights directly inside the apps that support your critical business functions. Enriching data through embedded intelligence. Image 1: Oracle’s Modern Data Platform Related Data Platform Products Oracle’s background in databases and data management over the last forty years has provided Oracle with industry-tested products that have transitioned to Oracle Cloud Infrastructure (OCI) and the Modern Data Platform. These products enable organizations to build out their modern data platforms quickly and with a known knowledge base. Get started with Oracle Modern Data Platform Oracle provides many options to get started on your Modern Data Platform journey. Use the links below to check out how Oracle Cloud Infrastructure (OCI) can be used to modernize your data platform! Free OCI Services (here) Learn with Step-by-Step guidance (here) Explore over 150 reference architectures (here) Estimate your costs (here) Contact RheoData to get started (here) today! Contact Info Give RheoData a call today to start architecting your Modern Data Platform.", "author" : { "@type" : "Person", "name" : "Bobby Curtis", "sameAs" : "", "url" : "https://rheodata.com/en-us/blog/author/bobby-curtis" }, "dateModified" : "10/11/2025", "datePublished" : "10/11/2025", "headline" : "Modern Data Platform with Oracle Cloud Infrastructure (OCI)", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/moderdataplatform-oci-1.png", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/moder-data-platform", "publisher" : { "@type" : "Organization", "logo" : { "@type" : "ImageObject", "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/RheoData%20-%20Logo%20-%20transparent-1.png" }, "name" : "RheoData", "url" : "rheodata.com" } } ``` ```json { "@context" : "http://schema.org", "@type" : "BlogPosting", "articleBody" : "A data pipeline is a method in which raw data or unchanged data is ingested from various data sources and shipped to another data store, like a relational database, data lake, or a data warehouse; where the data is eventually used for analysis. Before data is eventually used it undergoes some form of data processing or transformation; including filtering, masking, and aggregations. The transformation process ensures data integration and standardization. This is particularly important when the destination for the raw data is a relational database. As the names suggests, data pipelines act as the “piping” or “plumbing” for many different projects in modern data platforms, data science projects, or business intelligence dashboards. Data is can be and often sourced through a wide variety of places – APIs, SQL, NoSQL, flat files, etc., but the data is not ready for immediate use. Preparation of data usually falls on the shoulders of data engineers or data scientist, who structure the data to meet the need of the business and the associated use cases. The type of data processing that a data pipeline requires is usually determined through a mix of exploratory data analysis and defined business requirements. Once the data has been appropriately filtered, merged, and summarized, it can then be stored and used. Well-organized data pipelines provide the foundation for a range of data projects; this can include exploratory data analyses, data visualizations, and machine learning tasks. Types of Pipelines There are a few different types of data pipelines, but two primary types stand out; which are batch processing and stream processing. Batch Processing The development of batch processing was critical step in building data infrastructures that were reliable and scalable in the early days. This type of processing enabled organizations to move and process large amounts of data into repositories at set time intervals, typically during off-peak hours. This way workloads were not impacted as batch processing jobs tend to work with large volumes of data; taxing the overall system. Batch processing is the optimal data pipleline when there isn’t a immediate need to analyze a specific dataset and is more associated with the Extract, Transform, and Load (ETL) data ingestion process. Streaming Data Streaming data is leveraged when it is required for data to be continuously updated. For example, apps or point of sale (POS) systems need real-time data to update inventory and sales history of their products; that way, sellers can inform consumers if a product is in stock or not. A single action, like a product sale, is considered an “event”, and related events, such as adding an item to checkout, are typically grouped together as a “topic” or “stream.” These events are then transported via messaging systems or message brokers, such as the open-source offering, Apache Kafka. Since data events are processed shortly after occurring, streaming processing systems have lower latency than batch systems, but aren’t considered as reliable as batch processing systems as messages can be unintentionally dropped or spend a long time in queue. Message brokers help to address this concern through acknowledgements, where a consumer confirms processing of the message to the broker to remove it from the queue. Architecture of Data Pipelines There are three phases that make of a data pipeline. Data Ingestion Data Transformation Data Storage Within these three phases, data is moved and transformed as needed to ensure data can be used by an organization. Data Ingestion: Data is collected from various data sources, including various data structures (i.e. structured and unstructured data). Businesses can choose to extract data only when they are ready to process it; however, it is best practice to land raw data with a cloud provider first (data warehouse or data lake). This way, business can update historical data if they need to make adjustments to data processing routines. Data Transformation: A series of jobs are executed to process data and transform the data into a format that is required by the destination data repository. Transformation jobs embed automation and governance into the process flow, ensuring that the data is cleaned and transformed accordingly. Data Storage: After data is transformed, the data is then stored within a data repository (commonly, a relational database), where it can be exposed to business stakeholders. Data Pipelines vs ETL Pipelines In many circles, the terms of “data pipeline” and “ETL pipeline” are often interchangeable within a conversation; however, the term “ETL pipeline” should be considered a sub-category of the conversation. Between these two terms of a pipeline, there are distinguished points that need to be understood. ETL Pipelines: follow a specific sequence. As ETL implies, the pipeline extracts data, transform data, and then loads the data into a data repository. Not all data pipelines follow this sequence of events. In fact, changing the order of the processes with an ETL pipeline enables an ELT (Extract, Load, Transform) pipeline. ELT pipelines have be come popular with cloud-native approaches since they do the transformation later in the process. ETL pipelines also tend to imply the use of “batch processing”, but as noted earlier can also be inclusive of stream processing. Data Pipelines: It is unlikely to see a true data pipeline undergo data transformations, like an ETL pipeline. Data pipelines tend to be more focused on feeding data to the end target platform (relational database, data lake, or data warehouse), where additional processes will be used to do the data transformation. Data Pipeline Use Cases With the term “big data” being coined in the 1990’s, the growth of data has continued to grow and projected to reach 180 zettabytes by 2025 (2 years from now). As this growth of data continues, data management and data cleaning becomes an ever-increasing priority and putting more pressure on the use of “data pipelines”. While data pipelines can serve many different functions, the following, broad applications of them within business are mostly seen: Exploratory Data Analysis (EDA): EDA is used by data scientists to analyze and investigate data sets and summarized the sets main characteristics, often employing data visualization methods – making easier for data scientists to discover patterns, spot anomalies, test hypothesis, or check assumptions. Data Visualizations: Representations of data via common graphics (charts, plots, info graphs, etc.). Data visualizations display information and communication of complex data relations and data-driven insights in a way that is easy to understand. Machine Learning (ML/AI): Machine Learning is a sub-branch of Artificial Intelligence (AI) and compute science which focuses on using data and models to imitate the way a human learns, thinks, and gradually improves it accuracy. Through the usage of statistical models, models are trained to make classifications or predictions, uncover key insights within an organization’s data. RheoData Recommendations In the above discussion on Data Pipelines, there is a lot for organizations to think about and how these pipelines may or may not be in place with your organization. Organizations are often looking at the bigger picture or the goal, but not how to transform their existing “pipelines” into more modern approaches of getting data where it is needed. For these reasons, RheoData recommends using the following Oracle products to establish or refresh “data pipelines” and building analytical or machine learning/artificial intelligence (ML/AI) processes today. Oracle GoldenGate / Oracle GoldenGate Service Oracle GoldenGate Steram Processing Oracle Autonomous Data Warehouse / Oracle Autonomous Transaction Processing MySQL Heatwave These products from Oracle can help organizations build robust data pipeline, scalable data lake or data warehouse platforms and ensure timely data processing. Data Pipelines and RheoData RheoData has helped many customers, private and public sectors, gain understanding of their data pipelines and how various Oracle products can be used to enable organizational transformation. Below are a few examples: Shoe Carnival improves data pipeline by upgrading Oracle GoldenGate (here) Altec uses a hyper-volume data pipeline to ingest to Oracle Autonomous Data Warehouse (ADW)(here) American Tire Distributor using Oracle GoldenGate for Big Data to populate Google Cloud Storage (here) Zero-ETL – What is it? (here) Contact Info Give us a call today to schedule a review or build your data pipelines!", "author" : { "@type" : "Person", "name" : "Bobby Curtis", "sameAs" : "", "url" : "https://rheodata.com/en-us/blog/author/bobby-curtis" }, "dateModified" : "10/11/2025", "datePublished" : "10/11/2025", "headline" : "Data Pipelines – What is a data pipeline?", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/datapipelines-1.png", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/data-pipelines", "publisher" : { "@type" : "Organization", "logo" : { "@type" : "ImageObject", "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/RheoData%20-%20Logo%20-%20transparent-1.png" }, "name" : "RheoData", "url" : "rheodata.com" } } ``` ```json { "@context" : "http://schema.org", "@type" : "BlogPosting", "articleBody" : "If you work in the IT industry long enough, you start to see messaging or wording of items to repeat itself. In the case, we are going to take a look at the term “pipeline”. But what exactly is a “pipeline”? What is its purpose? How many different variations are there? In this post, we will take a look at the different types of pipelines there seems to be within the IT industry. What is a Pipeline? Data Pipeline In a general sense, a pipeline is a series of operations that are chained together to accomplish a goal. The output of one operation becomes the input of the next operation, until the desired goal is reached. This process can be visualized as a series of pipelines interconnected where data flows from one to the next; along the way data is either quickly moved or manupliated along the way. For example, suppose you want to move data from database A (source) to Kafka (target). The pipeline would look like: Capture all changed data/transactions Ship data/transactions Apply data/transactions to Kafka topic This is an over simplified example of a pipeline that is used to move data from a relational databases to a publication platform; yet it is considered a data pipeline. These types of “pipelines” can be chained together to build a data fabric or data mesh. Tools like Oracle GoldenGate or Oracle Cloud Infrastructure GoldenGate (OCI GoldenGate) can be used to build these pipelines between heterogenous platforms, on-premises and across clouds. Two types of Data Pipelines Batch Processing Batch processing is the most common data pipleline. This was a critical type of pipeline in the early years of the IT industry and enabled organizations to move large amounts of data from one system to another. This type of pipeline is not essential for analtyics, yet it is typically associated with ETL/ELT processes. In many cases, when batch processing is used timing of the execution is not critcal and often happens at night. Stream Processing Stream processing is starting to become the standard in the IT industry today. Through stream processing data is continously updated based on changes that occur between systems; also known as events. Data that is processed through this type of pipeline is stored in topic and can be subscribed to by outside system. Enabling quicker ingestion of data for organizational use. Elements of a Data Pipeline CI/CD Pipeline A Continous Integration/Continous Delivery (CI/CD) pipeline automates software delievery process from a software delievery point-of-view. This type of pipeline does the following: Builds code Runs tests (unit tests, QA tests, etc.) (CI) Deploys new versions of the application (CD) By building CI/CD pipelines, the building and delievery of applications are automated, removes manual errors, provide standard feedback to developers, and enables faster product iterations. Elements of a CI/CD Pipeline A CI/CD pipeline may seem to be more of an overhead process, but it is not. It should be viewed as a runnable specification of steps needed to deliver a software package between versions of releases. Without a CI/CD pipeline, developers and systems administrators would still need to perform the same steps in a manual process, hence being less productive. Most CI/CD pipeliens typically have the following stages: Faiure through any of the stages typically triggers a notification to let the responsible parties know about the cause. Otherwise, the only. notifications are sent after each successful deployment of the application. Machine Learning Pipelines If we take a look at Python with the Scikit-Learn packages, pipelining is used with machine learning processing and resolve issues like data leakage in testing setups. Pipelines, in this case, function by allwoing linear series of data transformations to be linked together resulting in a measurable modeling process. The objective is to gurantee that all phase within a pipeline are limited to the data available within the pipeline. The scikit-learn packages provides built-in functions for building pipelines (sklearn.pipeline & sklearn.make_pipeline), which simplifies the pipeline construction. A typical pipeline for Machine Learning using python with the scikit-learn package may look like the following: Loading Data Data Preprocessing Splitting of data Transformations Predictions and Evaluations The below image illistrates what the pipelien looks like in concept: AI Pipelines AI pipelines or machine learning pipelines (above) are interconnected or streamlined collection of operations. As data works though machine learning systems, the data is stored in collections and used to train models. Essentially, AI pipelines are “workflows” or interactive paths through which data moves through a machine learning platform. An AI pipeline/workflow is generallly made up of the following: Data Ingestion Data Cleaning Preprocessing Modeling Deployment The AI pipeline (workflow) moves information from collection to collection until it reaches the final deployment and represents an iterative process that continously feeds new information to machine learning/AI systems for AIs to learn and process. How does ML Pipelines share AI Pipelines? Understanding what AI Pipelines are, getting an understanding of what is happening is benefitical. There are seveal stages that AI has to work through as part of the “learning” process. These stages are: Preprocessing Learning Evaluation Prediction Each one of these stages are used by an AI as follows: Preprocessing – are the steps and methods that Machine Learning performs during initial modeling. These sub-steps include cleaning data, structuring data, and preparing it for AI learning Learning – is comprised of different models that are generated by Machine Learning algorithms. These different models are: Supervised Learning – providing machine learning algorithms with examples of expected output Unsupervised Learning – machine learning algorithms use data sets to learn about the inherent patterns in the data and how to best use the data for a specific task Reinforement Learning – action-and-reward teaching Deep Learning – teaching that uses layers of neural networks to facilitae machine learning for complext tasks like patter recognition for physical systems, facial and image recognition Evaluation – is driven by a “trained” brain that was created by machine learning algrothms and evaluated against data provided by the end user. At this stage, the AI is execpting to recieve information from the user that matches what the AI has been trained with. The below images illstrates what an AI pipeline looks like in concept: Summary In this artical, we learned that there a few different types of pipelines and that the industry term of “pipeline” can be used for many different topics and discussions. Although the term “pipeline” has become synonomous with many different processes, the core concept is still conveying data through a series of steps while acting upon that data in some way. As the industry and organizations begin to transition into more into the AI/LLM space, having a firm understanding of what type of pipelines are used will benefit and enable organizations to adopt new technolgoies and processes.", "author" : { "@type" : "Person", "name" : "Bobby Curtis", "sameAs" : "", "url" : "https://rheodata.com/en-us/blog/author/bobby-curtis" }, "dateModified" : "10/11/2025", "datePublished" : "10/11/2025", "headline" : "Different pipelines for the masses", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/Imported_Blog_Media/different_pipelines-1-1024x683-1.png", "width" : 750 }, "mainEntityOfPage" : "https://rheodata.com/en-us/blog/different-pipelines-masses", "publisher" : { "@type" : "Organization", "logo" : { "@type" : "ImageObject", "url" : "https://50642793.fs1.hubspotusercontent-na1.net/hubfs/50642793/RheoData%20-%20Logo%20-%20transparent-1.png" }, "name" : "RheoData", "url" : "rheodata.com" } } ```