25 items with this tag.
Are there any learnings from Chaos Engineering which can be used in any of the data projects like Data Engineering, Data Science etc. Can we use Chaos Engineering to fool-proof your end-to-end project?
Big data refers to extremely large and complex datasets that require advanced tools and techniques for storage, processing, and analysis.
Process to ensure that data is accurate, complete, reliable, and fit for its intended purpose throughout its lifecycle.
Data validation ensures the accuracy and quality of data by checking its compliance with defined rules and constraints before processing or storing it.
Databricks is a cloud-based platform that provides a unified environment for big data analytics and machine learning, built on Apache Spark.
An open-source cluster manager that abstracts resources across a cluster of machines, enabling efficient resource allocation and management for distributed applications
Yet Another Resource Negotiator (YARN) is a resource management and job scheduling framework used in Apache Hadoop for managing resources and running distributed applications on a cluster of machines.
An open-source framework designed for high-performance columnar data processing and efficient data interchange between systems.
A data serialization system that provides compact, fast binary data format and rich data structures for serializing, transporting, and storing data in a language-neutral way.
A highly efficient and optimized columnar storage file format used in the Hadoop ecosystem to improve performance in big data processing.
Change Data Capture (CDC) is a method used to automatically track and capture changes in data in a database, enabling real-time data integration and analysis.
A data lake is a centralized repository that stores large volumes of raw and unstructured data in its native format, enabling organizations to store diverse data types at scale and perform advanced analytics, machine learning, and other data processing tasks for insights and decision-making.
A data mart is a specialized subset of a data warehouse that focuses on specific business functions or departments, containing structured data optimized for analysis and reporting to support decision-making within those areas.
Data mesh is an architectural paradigm that advocates for a decentralized approach to data management, where data ownership, access, and governance are distributed across different domain-oriented teams, enabling scalability, flexibility, and agility in managing and leveraging data assets within organizations.
Iceberg tables are a high-performance, open table format for large analytic datasets that support complex data management and enable ACID transactions.
A columnar storage file format designed for efficient data processing, optimized for use with big data processing frameworks like Apache Spark and Apache Hadoop.
The process of extracting data from a data warehouse and loading it into operational systems, enabling organizations to leverage analytical insights in day-to-day operations.
A concept in data warehousing that refer to how data in a database changes over time while preserving historical information.
A powerful open-source unified analytics engine for large-scale data processing and machine learning, designed to handle both batch and streaming data efficiently.
Data engineering involves designing, building, and maintaining the infrastructure and systems that enable the acquisition, storage, processing, and analysis of data at scale, ensuring data quality, reliability, and accessibility for downstream analytics and applications.
A data lakehouse combines the benefits of a data lake (scalability, flexibility, and cost-effectiveness for storing raw and unstructured data) with those of a data warehouse (structured querying, transactional integrity, and performance optimizations), providing a unified platform for both operational and analytical workloads in modern data architectures.
A data pipeline is a series of processes that automate the flow of data from source systems to storage or analytical tools.
Learn about the inception of our unique framework, designed to streamline and democratize the Data Engineering process. Understand how this innovation in Data Engineering has enhanced our development workflow, promoting efficiency and collaboration. However, innovation isn't without its challenge.
Learn about the inception of our unique framework, designed to streamline and democratize the data engineering process. Understand how this innovation in data engineering has enhanced our development workflow, promoting efficiency and collaboration. However, innovation isn't without its challenges.
Explore the transformative potential of Low-Code/No-Code Data Engineering in this detailed blog post. Learn about the inception of our unique framework, designed to streamline and democratize the Data Engineering process. Understand how this innovation in Data Engineering has enhanced our development workflow, promoting efficiency and collaboration. However, innovation isn't without its challenges.