ADLS in Azure refers to Azure Data Lake Storage, a scalable and secure data storage solution provided by Microsoft Azure.
An AI agent is a software entity or program that perceives its environment, makes decisions, and takes actions autonomously to achieve specific goals.
Apache Hive is open-source software built for use in data warehousing. It allows for the analyzing and querying of vast amounts of data.
An API enables different software applications to communicate and share data.
An API Developer is a software engineer specialized in creating, maintaining, and implementing APIs (Application Programming Interfaces).
An API ecosystem is a network of interconnected APIs that enable different software applications, services, and platforms to communicate and interact.
API Functionality refers to the specific methods and data formats that an API can handle.
API Governance involves the set of practices that ensure the effective management and use of APIs.
An API (Application Programming Interface) is a set of programming tools used by engineers to integrate functionality offered by third parties. Using an API will let engineers develop software more quickly, with a richer set of features and less need for ongoing maintenance.
The API Lifecycle involves the stages an API goes through from creation to deprecation.
API management allows for the creation, assessment and analysis of APIs. Learn about API platforms and how they’re used in business.
An API Manager is a tool that manages and secures API traffic, facilitating the connection between different applications.
API monetization involves implementing strategies to charge users or developers for accessing and utilizing the API’s functionalities and data.
An API Portal is a centralized hub for managing and accessing APIs.
Application Rationalization is the process of assessing and optimizing an organization’s software applications.
An Automated Workflow is a series of automated actions that replace manual steps in a business process.
AWS Redshift is a cloud-based data warehouse and analytics service that is run by Amazon Web Services. Here is why AWS Redshift is so important.
Learn the purpose of Blob storage and how it’s commonly used by enterprises to store a variety of files.
Azure Data Lake is a part of Microsoft’s public cloud offering and allows for big data storage.
Big data architecture is the layout that underpins big data systems. Learn more about the common components of big data architecture here.
Big data ingestion gathers data and brings it into a data processing system where it can be stored, analyzed, and accessed.
Big data integration is the use of software, services, and/or business processes to extract data from multiple sources into coherent and meaningful information.
Big data maturity models (and analytics maturity models) help organizations leverage data trends and information to achieve specific measures of success.
Blob storage, short for Binary Large Object storage, is a solution designed to store massive amounts of unstructured data.
Business integration software is a system of applications and tools that aim at unifying data sets and business vectors for better oversight and centralization of governance.
A canonical data model is a standardized and simplified representation of data entities and relationships within an organization or across systems.
Change Data Capture is the process of capturing changes made to a database for further analysis or replication.
A cloud data warehouse is an online repository for all the data that an organization consolidates from various sources – data that can then be accessed and analyzed to run the business.
Learn about cloud integration strategy before you move your business data to the cloud.
Cloud speak is terminology that refers to cloud technology including acronyms and jargon.
Learn what cloud-based integration is and how it can help your business.
Cohort Analysis is a subset of behavioral analytics that groups data from a given dataset into related groups for analysis.
The composable architecture is an IT framework that allows enterprises to assemble modular, reusable components.
Continuous Data Protection (CDP) is a data backup and recovery system that automatically saves a copy of every change made to data.
CRM Integration involves the process of connecting a Customer Relationship Management system with other applications.
Data age customer service has had to adjust to the changing needs of business. As the enterprise digital transformation takes place, the services provided have to understand, predict, and provide for customer needs. New processes and quickly-evolving technology means that support has to have more insight and take a more proactive role in educating and caring for customer success.
Data aggregation is the process of collecting and combining data from different sources into a summarized format for analysis.
Get a basic understanding of data analytics and the role it plays in business.
Determining what is a valuable data asset and what is not is a complex process. Increasingly it is aided by automated programs and processes to sort through terabytes of big data.
Data Automation refers to the automating of data tasks to improve efficiency and reduce manual intervention.
A data catalog acts as a big data glossary, containing metadata references for the various tables, databases and files contained in data lakes or data warehouses.
Data Consolidation is the process of combining data from multiple sources into a single, unified view.
Data Extraction is the process of retrieving data from various sources for further processing or storage.
Data fabric is another variation of a distributed, decentralized data analytics and management framework as put forth by Gartner and is largely seen as a competing framework to data mesh.
Data federation is a data management strategy that involves creating a virtual database by integrating data from multiple disparate sources without moving the data from its original location.
Data Governance refers to the management and protection of data assets within an organization.
Data hydration, or data lake hydration, is the import of data into an object. When an object is waiting for data to fill it, this object is waiting to be hydrated. The source of that hydration can be a data lake or other data source.
Learn what a data ingestion pipeline is and how to leverage it for your business’s data needs.
Data integration is a foundational part of data science and analysis. Data can be overwhelming, providing too much data across sources to sort through to make timely, effective business decisions. Data integration sorts through large structured and unstructured data sets and selects data sets, structuring data to provide targeted insights and information.
Data integration is a task in and of itself. It often requires taking a business’s legacy processes which are central to a current system and then updating the system for modern digital users.
Learn what obstacles the healthcare industry is facing as it moves data to the cloud and how it can overcome them.
Learn what the primary data integration patterns are and which to use for your business’s data move to the cloud.
A Data Integration Platform is primarily used and governed by IT professionals. It allows data from multiple sources to be collected, sorted, and transformed so that it can be applied to various business ends or routed to specific users, business units, partners, applications, or prospective solutions.
The data integration process is the method through which a company combines data from several different platforms and datasets to make a cohesive, overarching digital architecture.
A data integration plan helps lay out a framework for digital transformation by incorporating the timelines, goals, expectations, rules, and roles that will encompass complete data integration.
Data integration strategies help discover and implement the most efficient, intelligent solutions to store, extract, and connect information to business systems and platforms.
A data integration strategy example is an overview of how data integration strategies work. Generally, this includes a list of certain elements of data integration strategies.
A data lake is a type of large-capacity data storage system that holds “raw” (semi- and unstructured i.e., streaming, IoT, etc.) data in its native format until needed. Unlike hierarchical data storage architectures, which store structured data in folders, a data lake employs a flat architecture.
Learn what products are available for managing the data lake to make the most of your business’s data.
A Data Lakehouse is a hybrid data platform that combines features of data lakes and data warehouses.
Data lineage refers to tracking and visualizing how data flows throughout its lifecycle, from origin to final destination.
A data mart is a specific subset of data held in a data warehouse and allows specific departments to find the data they need easier and faster.
Data mesh is an enterprise data management framework that defines how to manage business-domain-specific data in a way that allows business domains to own and operate their data.
Data mesh and data fabric are data analytics and management frameworks that are largely similar and overlapping, but with a few areas of distinction.
Data migration tools assist teams in their data migration efforts, including on-premises data migration, cloud-based data migration and open-source data migration.
Data Mining is a key technique in data science that involves extracting valuable insights from large data sets. It is essential for pattern recognition, information extraction, and knowledge discovery, playing a critical role in data-driven decision-making across various industries.
Data obfuscation is a security technique that involves altering sensitive data to protect it from unauthorized access while maintaining its usability.
A data pipeline is a service or set of actions that process data in sequence. The usual function of a data pipeline is to move data from one state or location to another.
A data pipeline architecture is a system that captures, organizes, and routes data so that it can be used to gain insights. Raw data contains too many data points that may not be relevant.
A Data Platform is a technology solution designed for storing, managing, and analyzing data.
Data profiling is the process ofanalyzing data from existing information sources to collect statistics and information about the structure, content, and quality of the data.
Data provenance refers to the detailed history and lineage of a piece of data, including information about its origins, transformations, and movements.
Data quality metrics are essential standards used to evaluate the condition and suitability of data for specific purposes.
Data Replication is the process of copying data to ensure consistency across multiple locations or systems.
Data sovereignty refers to the concept that data is subject to the laws and governance structures of the country in which it is collected or stored.
A data swamp is a term used to describe a mismanaged data repository that makes data analysis and data-driven decision-making difficult.
Data synchronization ensures that data across different devices, systems, or applications is consistent and up-to-date. This helps maintain data integrity.
Data Virtualization involves creating a virtual layer that sits between the data sources and the applications that use the data.
Learn which technology companies can provide data warehouse services for your business’s data storage needs.
The vast popularity and opportunities arising from data warehouses have encouraged the development of numerous data warehousing tools.
A Database is a structured collection of data that can be easily accessed, managed, and updated.
Database Replication involves creating and maintaining duplicate versions of a database to ensure data consistency and availability.
A Database Schema is a blueprint that outlines the structure of a database, including tables, fields, and relationships.
Learn all about deep learning – from its relationship with machine learning to how its application is rising in many fields.
A digital-only customer is exactly what it sounds like – a customer that a company engages with on any sort of non-physical level. In turn, digital customers come with their own set of company best practices.
Digital Marketing Analytics involves the measurement, collection, and analysis of marketing data to optimize digital marketing strategies.
EAI, or Enterprise Application Integration, is a framework for connecting different enterprise applications to enable data sharing and process automation.
EDI, or Electronic Data Interchange, is a method for transferring data between different systems without human intervention.
An Enterprise Architect is a professional responsible for designing and managing an organization’s IT framework.
An Enterprise Data Warehouse is a large-scale database that consolidates business data from various sources for reporting and analysis.
Enterprise Resource Planning (ERP) is a type of software that allows an organization to manage and automate many of its day-to-day business activities.
An enterprise service bus (ESB) is an architecture which allows for communication between different environments, such as software applications.
A data integration process involving extraction, transformation, and loading.
An ETL Pipeline is a set of processes for extracting, transforming, and loading data from one system to another.
The ETL process involves extracting data from source systems, transforming it into a format that can be analyzed, and loading it into a data warehouse.
ETL Testing involves the process of validating, verifying, and qualifying data while preventing duplicate records and data loss.
GenAI, short for Generative Artificial Intelligence, refers to AI systems capable of generating new content, ideas, or data that mimic human-like creativity.
GenAI applications refer to the practical use of Generative Artificial Intelligence technologies across various industries and sectors.
Generative AI is a form of artificial intelligence that can create new data similar to the data it was trained on.
Generative Integration is an advanced approach to data and application integration that leverages Generative AI and Large Language Models (LLMs).
Guidewire is a platform offering core back-end software for the property and casualty insurance industry.
A Hadoop data lake is built on a platform made up of Hadoop clusters and is particularly popular in data lake architecture as it is open source.
The uptake or data, or ingestion, for data storage, sorting, and analysis is an ongoing process at the base of system architecture and data management. The rate of ingestion is part of creating real-time data insights and a competitive advantage for business strategy.
The advantages of Hive allow for easier integration with custom elements, like extensions, programs, and applications. It is also better suited for batch data ingestion and processing.
Human-in-the-loop is an approach to artificial intelligence and machine learning where human oversight is embedded within the decision-making or learning process.
Hyperautomation involves the use of advanced technologies like AI and machine learning to automate complex business processes.
An Integration is the process of combining different systems and applications to work together.
Application integration software, generally classed as “middleware”, i.e. software that forms the joining link between interfacing operating systems, software and databases.
Learn about best practices for cloud integration and how they can help your business.
Integration Platform as a Service (IPaaS) is a cloud-based integration system that connects software applications from different environments including devices, IoT, applications, et cetera
An Integration Requirements Document assesses and describes the requirements for a successful data integration. Much like a Systems/Software Requirements Specification (SRS), the Integration Requirements articulates the expected behavior and features of the integration project and related systems. Most Integrations Requirements Documents are part of a larger Data Integration Requirement Plan and quality of service standards.
Explore how the Internet of Things (IoT) uses AI and GenAI to transform industries with smart, connected devices and real-time data.
Learn what iPaaS architecture is and how it can help you move your business’s digital infrastructure to the cloud.
A Java performance test checks a Java application’s performance for speed and efficiency.
Learn what Java speed test code is and what it is used for.
In this article we provide a brief description of JSON and explain how it’s primarily used.
A Large Language Model (LLM) is a type of artificial intelligence that processes and generates human-like text based on vast amounts of data.
Legacy system integration is the process of connecting and enabling communication between older, often outdated systems and newer systems or technologies.
The Oracle E-Business Suite is a set of integrated business applications provided by Oracle.
Here’s everything you ever wanted to know about a machine learning algorithm.
Master data management software cleans and standardizes data, creating a single source of truth. Learn about the many advantages of using data management software.
Learn where Microsoft Azure Storage servers are located and how their co-locations can help your business.
The Modern Data Stack is a set of technologies optimized for rapid data integration, transformation, and analysis.
MongoDB is a NoSQL database used for handling large volumes of unstructured data.
Moving data to the cloud, also known as Cloud Migration, is when data that is kept on state-side servers, personal/physical servers, is relocated to a completely digital storage platform.
Natural language processing is a field of artificial intelligence (AI) that focuses on the interaction between computers and human (natural) languages.
A Network Application is software that performs functions over a network, such as file sharing.
In computing and technology, the term “node” can refer to several different concepts depending on the context. Check out these common definitions.
Orchestration involves the automated arrangement and coordination of complex tasks within a workflow.
Prescriptive Analytics is a type of analytics that uses data and algorithms to provide recommendations for ways to handle potential future situations.
Extracting data from Salesforce with Informatica may not be the best data extraction and integration solution.
Python vs. Java: Python and Java are both programming languages, each of which has its advantages. The most significant difference between the two is how each uses variables. Python variables are dynamically typed whereas Java variables are statically typed.
Python and Java are two of the most popular and robust programming languages. Learn the differences in this Java vs. Python performance comparison.
Real-time data replication is the near-instantaneous duplication and synchronization of data across multiple systems to ensure consistency, high availability, and support for disaster recovery in diverse environments.
Representational State Transfer is an architectural style for software that provides a set of principles, properties and constraints for standardizing operations built on http.
Retrieval Augmented Generation is a machine learning approach that combines the strengths of retrieval-based methods and generative models to enhance the quality and relevance of generated text.
Robotic Process Automation (RPA) involves the use of software robots to automate repetitive tasks in business processes.
RPA in Healthcare refers to the application of Robotic Process Automation in healthcare settings for tasks like billing and data entry.
SAP Analytics is a system that uses predictive cloud analytics to predict future outcomes, allowing data analysts and business stakeholders to make informed decisions.
SAP integrations take data from one source (such as an application or software) and make it readable and usable in SAP.
Schema drift refers to the gradual changes that occur in the structure, format, or organization of data within a database or data system over time.
A semantic data layer is a conceptual layer in an information system that adds meaning and context to raw data, enabling more intuitive and efficient data access, integration, and analysis.
Semantic search refers to a search technology that aims to improve search accuracy by understanding the contextual meaning of search terms.
Sentiment analysis is the process of using NLP, text analysis, and computational linguistics to identify and extract subjective information from text data.
Snowflake database is a data warehouse in the cloud solution powered by Snowflake’s software.
Software integration allows for the joining of multiple types of software sub-systems to create one single unified system.
Find out how Spark SQL makes using Spark faster and easier.
What is a Splunk query? Learn how it makes machine data accessible, usable and valuable to everyone.
SQL Azure is a managed relational database service provided by Microsoft Azure.
SQL in Data Analysis refers to the use of SQL (Structured Query Language) for querying and manipulating data for analysis purposes.
SQL server functions are sets of SQL statements that execute specific tasks, allowing for common tasks to be easily replicated.
SSL authentication stands for Secure Sockets Layer and is a protocol for creating a secure connection for user-server interactions.
The REST framework used for network-based applications. It relies upon a client-based stateless server. An SSL authentication assures that interactions between client and server are secure by encrypting the link that connects them.
Transactional Database is a specialized type of database designed to handle a high volume of transactions. It ensures data integrity and supports real-time processing, making it indispensable for applications like online banking and e-commerce.
Traditional relational data warehouses are increasingly being complemented by or transitioned to non-relational big data. With the change to big data, new skillsets, approaches, and technologies are required.
A two-tiered LLM structure refers to an advanced AI architecture involving two distinct layers of large language models that work sequentially or collaboratively to interpret user intent and refine responses.
In two-way SSL, AKA mutual SSL, the client confirms the identity of the server and the server confirms the identity of the client.
Vector databases store and search high-dimensional data. Learn their AI use cases, technical details, and integration for scalable, efficient apps.
Vector embeddings are a type of representation that converts high-dimensional data into a continuous vector space.
Vector indexing is a technique in machine learning and data retrieval that’s used to efficiently organize and search large sets of high-dimensional vectors.
Vibe coding is a creative, intuitive approach to programming that prioritizes flow and experimentation over structure and planning.
A VPC, or Virtual Private Cloud, is a secure, isolated virtual network in a public cloud environment.
The Workday Cloud Connect for Benefits is an enterprise management suite that offers a single system to manage your employee benefits.
The Workday EIB (Enterprise Interface Builder) is a tool that supplies users with both a guided and graphical interface.
Workday provides single-architecture, cloud-based enterprise applications and management suites that combine finance, HR, and analytics into a single system.