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IOMETE glossary

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ACID Transactions

ACID is an acronym representing the four essential properties defining a transaction - Atomicity, Consistency, Isolation, and Durability.
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AdaGrad

AdaGrad is a family of sub-gradient algorithms for stochastic optimization. Gradient descent is the most commonly used optimization method in machine learning and deep learning algorithms.
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Alternative Data

Alternative data refers to information gathered from non-traditional sources, providing insights beyond what traditional data sources can offer. The definition of alternative data varies across industries, depending on the conventional data sources used by businesses and competitors.
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Anomaly Detection

Anomaly detection is a powerful technique used to identify rare events or observations that differ significantly from the rest of the data. This technique is particularly useful in finance, where thousands or millions of transactions need to be monitored for potential errors or fraudulent activity.
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Apache Hadoop

Apache Hadoop is an open source, Java-based software platform that manages data processing and storage for big data applications. The platform works by distributing Hadoop big data and analytics jobs across nodes in a computing cluster, breaking them down into smaller workloads that can be run in parallel.
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Apache Hive

Apache Hive is an open-source data warehouse solution designed to read, write, and manage large datasets extracted from the Apache Hadoop Distributed File System (HDFS), which is part of a broader Hadoop Ecosystem.
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Apache Kudu

Apache Kudu is a free and open-source columnar storage system developed for Apache Hadoop. This engine is designed for structured data and supports low-latency random access to individual rows, along with efficient analytical access patterns. Kudu bridges the gap between the widely used Hadoop Distributed File System and HBase NoSQL Database.
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Apache Kylin

Apache Kylin is a distributed open-source online analytics processing (OLAP) engine designed for interactive Big Data analytics. It provides a SQL interface and multi-dimensional analysis (OLAP) on Hadoop and Spark and easily integrates with BI tools through ODBC drivers, JDBC drivers, and REST APIs.
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Apache Spark as a Service

Apache Spark is an open source cluster computing framework for fast real-time large-scale data processing. IOMETE Spark-as-a-Service comes with a set of built-in applications that can help you access and analyze data faster.
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Artificial Neural Network (ANN)

An artificial neuron network (ANN) is a computing system modeled after the operation of neurons in the human brain.
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Automation Bias

Automation bias is an over-reliance on automated aids and decision support systems, while machine bias relates to the ways in which algorithms exhibit the bias of the algorithm used or their input data.
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Bayesian Neural Network (BNN)

Bayesian Neural Networks (BNNs) are a type of neural network that uses statistical methodology to attach a probability distribution to everything, including model parameters. By extending standard networks with posterior inference, BNNs can control over-fitting and obtain better results for a vast number of tasks, especially in domains where data is scarce, such as molecular biology and medical diagnosis.
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Big Data Analytics

Big data analytics is the often complex process of examining large and varied data sets - or big data - that has been generated by various sources such as eCommerce, mobile devices, social media, and the Internet of Things (IoT). It involves integrating different data sources, transforming unstructured data into structured data, and generating insights from the data using specialized tools and techniques that spread out data processing over an entire network.
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Bioinformatics

Bioinformatics is a rapidly growing field that uses computation to extract knowledge from vast collections of biological data. It involves the use of IT in biotechnology for storing, retrieving, organizing, and analyzing biological data. With an enormous amount of data being generated by genome sequencing projects and other studies, bioinformatics has become essential for managing data in modern biology and medicine.
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Catalyst Optimizer

Spark SQL's Catalyst optimizer is a powerful tool that uses advanced programming language features to build an extensible query optimizer. Catalyst is based on functional programming constructs in Scala and is designed to easily add new optimization techniques and features to Spark SQL.
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Complex Event Processing (CEP)

Complex Event Processing (CEP) is a technology used for querying data before storing it in a database or analyzing it in real-time without ever storing it. CEP is an organizational tool that helps to aggregate different information and identify cause-and-effect relationships among events in real-time, allowing you to proactively take effective actions.
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Continuous Applications

Continuous applications are a type of end-to-end application that reacts to data in real-time. Developers can use a single programming interface to support the different aspects of continuous applications, such as query serving or interaction with batch jobs.
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Convolutional Layer

A convolutional neural network (CNN or ConvNet) is a type of deep neural network used for recognizing patterns in images, as well as for spatial data analysis, computer vision, natural language processing, signal processing, and more.
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Data Analysis Platform

A data analysis platform is a collection of technologies and services used to analyze large, complex, and dynamic datasets. It allows you to retrieve, combine, interact with, explore, and visualize data from various sources.
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Data Automation

Data automation is a popular data management technique that can help organizations collect, upload, transform, store, process, and analyze data without the need for manual human intervention.
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Data Governance

Data governance is a comprehensive approach that comprises the principles, practices, and tools to manage an organization's data assets throughout their lifecycle. By aligning data-related requirements with business strategy, data governance provides superior data management, quality, visibility, security, and compliance capabilities across the organization.
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Data Lakehouse

A data lakehouse is a data repository that combines the best features of data warehouses and data lakes. Enabled by a new, open system design, data lakehouses implement similar data structures and management features to those in a data warehouse directly on the low-cost storage used for data lakes.
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Data Mart

A data mart is a curated database designed to serve the specific needs of a single data team, community, or line of business, such as the marketing or engineering department.
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Data Pipelines

A data pipeline is a series of steps that move data from one system to another, encompassing the source, processing steps, and destination. Data can be modified during the transfer process, and some pipelines may be used to transform data.
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Data Sharing

Data sharing refers to the ability to make the same data available to one or many stakeholders. It enables new business opportunities, collaboration with partners, and the generation of new revenue streams through data monetization.
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Data Transformation

Data transformation is the process of turning raw data into usable datasets that organizations can use to meet operational needs and create actionable insights. By manipulating datasets, the data transformation process generates insights that can be used to make business decisions.
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Data Vault

A Data Vault is a data modeling design pattern used to build a data warehouse for enterprise-scale analytics. It is made up of three types of entities - hubs, links, and satellites.
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Data Warehouse

A data warehouse is a centralized repository that stores current and historical data from multiple sources in a business-friendly manner for easier insights and reporting. Data warehouses are typically used for business intelligence (BI), reporting, and data analysis, making them essential for businesses looking to optimize their decision-making processes.
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DataFrame

A DataFrame is a two-dimensional table of rows and columns, similar to a spreadsheet, that organizes data in a structured way. It is one of the most commonly used data structures in modern data analytics due to its flexibility and ease of use.
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Dataset

A dataset is a structured set of data gathered and stored for analysis or processing. Typically, the data within a dataset is related and originates from a single source or is intended for a specific project.
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Deep Learning

Deep Learning is a powerful subset of machine learning that uses algorithms inspired by the human brain to analyze large amounts of data. Also known as deep neural networks, these models are designed to learn data representations, rather than relying on traditional algorithms.
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Demand Forecasting

Demand forecasting is the process of projecting consumer demand to estimate future revenue. It involves projecting the assortment of products that shoppers will buy using both quantitative and qualitative data.
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Dense Tensor

Dense tensors are a crucial component of multi-dimensional data analysis applications. They store values in a contiguous sequential block of memory, making them efficient for tensor computations. Popular software products like MATLAB and open source third-party toolboxes offer support for dense tensor operations.
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Digital Twin

A digital twin is a virtual model that accurately reflects a physical object. In manufacturing, a digital twin gathers system and process state data with the help of various IoT sensors and enterprise data to form a virtual model.
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DNA Sequencing

DNA sequencing is the process of determining the exact sequence of nucleotides in DNA (deoxyribonucleic acid). This involves determining the order of the four chemical building blocks - adenine, guanine, cytosine, and thymine - that make up the DNA molecule.
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Extract Transform Load (ETL)

ETL, or extract, transform, and load, is a critical process for businesses looking to derive insights from their data. As data sources and types continue to grow, data engineering teams face increasing pressure to process raw data into clean, reliable data that can be used downstream.
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Feature Engineering

Feature engineering is a critical process in machine learning that involves transforming raw data into features that can be used to train models. By improving the quality of the input data, feature engineering can help create more accurate and efficient models.
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Hadoop Cluster

Apache Hadoop is an open-source, Java-based software framework and parallel data processing engine. It allows big data analytics processing tasks to be divided into smaller tasks that can be executed in parallel using an algorithm (such as the MapReduce algorithm) and distributed across a Hadoop cluster.
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Hadoop Ecosystem

The Apache Hadoop ecosystem encompasses various components of the Apache Hadoop software library, consisting of open-source projects and a wide range of complementary tools. Some well-known tools in the Hadoop ecosystem include HDFS, Hive, Pig, YARN, MapReduce, Spark, HBase, Oozie, Sqoop, Zookeeper, and more.
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Hash Buckets

Hash Buckets, also known as hash tables or hash maps, are a powerful data structure in computing that allows for quick and efficient access to objects based on a unique key, such as a Social Security Number or account number.
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HDFS

HDFS, short for Hadoop Distributed File System, is the primary storage system for Hadoop applications. This open-source framework facilitates the swift transfer of data between nodes and is commonly employed by companies dealing with and storing big data.
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Hive Date Function

Hive is a powerful tool for processing and querying data, and it comes with a wide range of built-in functions to help you get the most out of your data. These functions include string manipulation, date manipulation, mathematical functions, conditional operators, and more.
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Hosted Spark

Hosted Spark is a unified data platform that provides organizations with a simplified way to interact with Apache Spark, a fast and general cluster computing system for Big Data.
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Jupyter Notebook

A Jupyter Notebook is an open-source web application that allows data scientists to create and share documents containing live code, multimedia elements, and equations.
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Keras Model

Keras is a powerful high-level library for deep learning built on top of Theano and Tensorflow. It provides an easy and efficient way to create a range of deep learning models in Python. Keras has become one of the most popular high-level neural network APIs for developing and testing neural networks.
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Lambda Architecture

Lambda Architecture is a powerful solution for processing large amounts of data, commonly known as "Big Data". It combines batch-processing and stream-processing methods to compute arbitrary functions. The architecture consists of three layers - the batch layer, the serving layer, and the speed layer.
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Large Language Model Ops (LLMOps)

Large Language Model Ops (LLMOps) refers to a set of practices, techniques, and tools used for managing large language models in production environments. LLMOps is becoming increasingly vital as enterprises deploy large language models, including notable examples like OpenAI's ChatGPT and Google's Bard.
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Large Language Models (LLMs)

Large Language Models (LLMs) are revolutionizing the field of natural language processing (NLP). These advanced machine learning models are trained on massive datasets to understand the patterns and structures of human language.
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Machine Learning Library (MLlib)

Apache Spark's Machine Learning Library (MLlib) is a powerful and scalable machine learning library designed to work seamlessly with other Spark components. It provides data scientists with a user-friendly platform to tackle complex distributed data challenges, allowing them to focus on solving data problems and building models.
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Machine Learning Model

A machine learning model is a powerful tool that leverages algorithms to identify patterns and make decisions based on previously unseen data. In various fields, such as natural language processing and image recognition, machine learning plays a crucial role in optimizing algorithms to discern specific patterns or outputs.
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Managed Spark

Managed Spark is a dynamic service designed to empower users with open source data tools for diverse tasks such as batch processing, querying, streaming, and machine learning. This service facilitates the rapid creation of clusters on-demand, streamlined cluster management, and seamless scalability, enabling users to efficiently handle data-intensive workloads.
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MapReduce

MapReduce is a potent distributed execution framework within the Apache Hadoop Ecosystem. It simplifies distributed programming by exposing two processing steps - Map and Reduce. By leveraging MapReduce, you can split data between parallel processing tasks and apply transformation logic to each chunk of data. Once completed, the Reduce phase aggregates data from the Map set.
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Materialized Views

Materialized views differ from regular database views as they store precomputed data as a physical table. This feature makes them well-suited for handling complex queries or aggregations that are frequently performed.
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Medallion Architecture

Managed Spark a dynamic service with open-source data tools empowering users for diverse tasks like batch processing, querying, streaming, and machine learning.
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ML Pipelines

Machine learning algorithms involve multiple stages, from pre-processing to feature extraction, model fitting, and validation. Connecting these stages, especially with large-scale datasets, can be challenging. Many ML libraries are not optimized for distributed computation or pipeline creation and tuning.
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MLOps (Machine Learning Operations)

MLOps (Machine Learning Operations) is a critical function in Machine Learning engineering that optimizes the process of taking machine learning models to production and ensures their ongoing maintenance and monitoring.
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Model Risk Management

Model Risk Management is a crucial facet of risk management and operational efficiency, especially for financial institutions.
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Neural Network

Neural networks are a type of computing model that mimics the networked structure of neurons in the brain. They consist of interconnected processing elements called neurons that work together to produce an output function. Neural networks are made up of input and output layers and often include a hidden layer that transforms input into something the output layer can use.
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Open Banking

Open Banking is a revolutionary way to provide secure access to consumers' financial data, all with their consent.
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Orchestration

Data Orchestration is the process of coordinating and managing multiple computer systems, applications, and services to execute larger workflows or processes.
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Overall Equipment Effectiveness

Overall Equipment Effectiveness is a measure of how well a manufacturing operation is being utilized compared to its full potential. By identifying the percentage of manufacturing time that is truly productive, one can gain important insights on how to systematically improve the manufacturing process.
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Pandas DataFrame

Pandas DataFrame is an open-source library, written for the Python programming language, that offers fast and adaptable data structures and analysis tools perfect for scientific computing, machine learning, and data science.
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Parquet

Apache Parquet is an open-source, column-oriented data file format designed for efficient data storage and retrieval. It offers effective data compression and encoding schemes, enhancing performance when handling large amounts of complex data. Apache Parquet serves as a common interchange format for both batch and interactive workloads.
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Personalized Finance

Personalized Finance - also known as open finance - is a data-driven approach that empowers banks to offer tailored services to their clients.
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Predictive Analytics

Predictive Analytics is a powerful tool that uses advanced techniques to analyze both new and historical data, identifying patterns and predicting future outcomes and trends. By leveraging big data and applying algorithms to large data sets, organizations can find and exploit patterns within their data to detect risks and opportunities.
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Predictive Maintenance

Predictive Maintenance is a method of determining when an asset needs maintenance based on its actual condition, rather than a fixed schedule. By predicting and preventing failures, you can reduce costly equipment downtimes and maximize uptime and productivity.
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PyCharm

PyCharm is a popular integrated development environment (IDE) designed for the Python programming language. It is commonly used in computer programming.
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PySpark

PySpark is a Python API for Apache Spark, designed to support the collaboration of Spark and Python. With PySpark, you can interface with Resilient Distributed Datasets (RDDs) in Apache Spark using Python programming language.
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Real-Time Analytics

Real-time Analytics is an essential tool for businesses that require swift, data-driven decision-making.
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Real-Time Retail

Real-time data provides accurate and timely insights that enable businesses to make informed decisions and improve supply chain agility, reduce costs, optimize product availability, and replenish stock.
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Resilient Distributed Dataset (RDD)

RDDs have been the primary user-facing API in Spark since its inception. At their core, RDDs are immutable distributed collections of data elements that are partitioned across nodes in a cluster, allowing for parallel operation using a low-level API that offers transformations and actions.
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Retrieval Augmented Generation

Retrieval Augmented Generation (RAG) is an innovative approach that can enhance the effectiveness of large language model (LLM) applications by leveraging custom data.
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Snowflake Schema

Snowflake Schema is a multidimensional data model used for business intelligence and reporting in data warehouses, data marts, and relational databases. It is an extension of a Star Schema, where dimension tables are broken down into subdimensions.
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Spark API

If you're working with Spark, you'll encounter three APIs - DataFrames, Datasets, and RDDs (Resilient Distributed Datasets).
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Spark Applications

Spark Applications are made up of a driver process and a set of executor processes. The driver process is responsible for maintaining information about the application, responding to user input, and distributing work across the executors.
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Spark Elasticsearch

Spark Elasticsearch is a powerful, open-source, NoSQL distributed database that stores, retrieves, and manages document-oriented and semi-structured data. Used by thousands of top companies, including tech giants like Google, Oracle, and Microsoft, Elasticsearch is a flexible solution for indexing structured data.
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Spark SQL

Spark SQL is a powerful module for structured data processing that provides native support for SQL in Spark. It enables users to run unmodified Hadoop Hive queries up to 100x faster on existing deployments and data, while also providing seamless integration with the rest of the Spark ecosystem.
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Spark Streaming

Spark Streaming is a powerful and scalable streaming processing system that supports both batch and real-time workloads. However, it is now considered a legacy project with no further updates.
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Spark Tuning

Spark Tuning refers to the process of adjusting settings to optimize memory, cores, and instances utilized by the system, ensuring seamless performance and avoiding resource bottlenecks in Apache Spark.
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Sparklyr

Sparklyr is an open-source package that bridges the gap between R and Apache Spark, allowing you to harness Spark's distributed data capabilities within a modern R environment. This powerful tool enables interactive manipulation of large datasets, making it ideal for data manipulation, analysis, and visualization.
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SparkR

SparkR is a powerful tool for running R on Apache Spark, offering the same principles as Spark's other language bindings. With SparkR, users can easily import it into their environment and run their code, making it a great option for those familiar with R's syntax.
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Sparse Tensor

Sparse Tensor is a powerful tool for handling multi-dimensional arrays and serves as a vital component in developing the pytensor library. One of the key classes in pytensor is the Sptensor, which represents sparse tensors.
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Star Schema

A star schema is a multidimensional data model used to organize data in a database, making it easy to understand and analyze. Star schemas can be applied to data warehouses, databases, data marts, and other tools. The star schema design is optimized for querying large data sets.
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Streaming Analytics

Streaming Analytics, also referred to as event stream processing, is an effective method for examining real-time data. By employing continuous queries known as event streams, organizations can derive valuable insights from various data sources such as the Internet of Things (IoT), cloud applications, mobile devices, and machine sensors.
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Structured Streaming

Structured Streaming is a powerful stream processing API that was introduced in Spark 2.2. With Structured Streaming, one can perform the same operations that one does in batch mode using Spark's structured APIs, but in a streaming fashion. This can significantly reduce latency and enable incremental processing.
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TensorFlow

TensorFlow is an open-source library that streamlines the development of machine learning models. It empowers developers to train and deploy deep neural networks for tasks like image recognition and natural language processing. TensorFlow supports CPUs, GPUs, and Google's tensor processing units (TPUs), with wrappers available in multiple languages, including Python, C++, and Java.
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Tensorflow Estimator API

The Tensorflow Estimator API is a powerful tool for creating machine learning models that are both complete and intuitive. With the Estimator API, one can easily train, evaluate, and generate predictions for one’s model.
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Tungsten Project for Apache Spark

The Tungsten Project is a major initiative to enhance Apache Spark's execution engine. Its goal is to significantly boost the efficiency of memory and CPU usage for Spark applications, bringing performance closer to the limits of modern hardware.
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Unified Data Analytics

Unified Data Analytics is a cutting-edge solution that combines data processing and AI technologies, making AI more accessible for enterprises and accelerating their AI initiatives.
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Unified Data Analytics Platform

A Unified Data Analytics Platform assists organizations in accelerating innovation by integrating data science, engineering, and business. With IOMETE as your Unified Data Analytics Platform, you can rapidly prepare and clean data on a massive scale without limitations.
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Unified Data Warehouse

A centralized database that consolidates all business information, making it readily available throughout the company.
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What Is Apache Spark?

Apache Spark is an open-source analytics engine for processing large data sets. It can handle batch and real-time analytics and data processing tasks. Spark began in 2009 as a research project at the University of California, Berkeley, aiming to improve processing speeds in Hadoop systems.
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What is Data Security?

Data security is a set of practices and procedures designed to protect data from unauthorized access, theft, corruption, poisoning, or accidental loss. It involves safeguarding sensitive data, such as personally identifiable information, financial information, health information, and intellectual property, throughout the data lifecycle, from creation to destruction.
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