Glossary

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 (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.

API management allows for the creation, assessment and analysis of APIs. Learn about API platforms and how they’re used in business.

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.

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 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.

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.

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.

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 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 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.

Data ingestion gathers data and brings it into the data processing systems. The data ingestion layer processes incoming data, prioritizing sources, validating data, and routing it to the best location to be stored and be ready for immediately access.

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 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 industry buzzword and a comprehensive design approach for implementing a modern, distributed-data analytics and data sharing architecture at scale.

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.

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.

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. ​

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.

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.

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.

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.

Generative Integration is an advanced approach to data and application integration that leverages Generative AI and Large Language Models (LLMs).

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.

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.

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.

Java and Python are two of the most popular programming languages, but their different language structures have distinct advantages and disadvantages. Each has numerous ecosystem tools built for better usability and performance benefits. However, their speeds vary.

In this article we provide a brief description of JSON and explain how it’s primarily used.

JSON Web Token (JWT) is a JSON object that is used to send information that can be verified and trusted. The standard is open-source, based on RFC 7519

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.

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.

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.

Representational State Transfer is an architectural style for software that provides a set of principles, properties and constraints for standardizing operations built on http.

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.

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 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.

In two-way SSL, AKA mutual SSL, the client confirms the identity of the server and the server confirms the identity of the client.

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.