Snowflake

JANUARY 23, 2023

A conventional data platform implemented in the cloud is still bound by the same limitations—the complexity of the platform, the need for extraction, transformation, and loading, and an inability to ingest both structured and semi-structured data. Of that group, 75.7%

Data Integration 72

Data Integration Data Data Warehouse Cloud 72

AltexSoft

NOVEMBER 10, 2021

At the same time, it brings structure to data and empowers data management features similar to those in data warehouses by implementing the metadata layer on top of the store. Traditional data warehouse platform architecture. Data lake architecture example. Poor data quality, reliability, and integrity.

Architecture 52

Architecture Data Lake Data Warehouse Metadata 52

AltexSoft

MARCH 30, 2023

What is Databricks Databricks is an analytics platform with a unified set of tools for data engineering, data management , data science, and machine learning. It combines the best elements of a data warehouse, a centralized repository for structured data, and a data lake used to host large amounts of raw data.

Scala 64

Scala Data Lake Machine Learning BI 64

AltexSoft

JUNE 26, 2023

Data collection is a methodical practice aimed at acquiring meaningful information to build a consistent and complete dataset for a specific business purpose — such as decision-making, answering research questions, or strategic planning. Key differences between structured, semi-structured, and unstructured data.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

AltexSoft

SEPTEMBER 23, 2021

The main purpose of a DW is to enable analytics: It is designed to source raw historical data, apply transformations, and store it in a structured format. This type of storage is a standard part of any business intelligence (BI) system, an analytical interface where users can query data to make business decisions.

Architecture 52

Architecture Data Lake Unstructured Data Data Warehouse 52

phData: Data Engineering

SEPTEMBER 19, 2023

Cost reduction by minimizing data redundancy, improving data storage efficiency, and reducing the risk of errors and data-related issues. Data Governance and Security By defining data models, organizations can establish policies, access controls, and security measures to protect sensitive data.

Data Lake 52

Data Lake Process Metadata Data Warehouse 52

ProjectPro

JANUARY 27, 2023

Top ETL Business Use Cases for Streamlining Data Management Data Quality - ETL tools can be used for data cleansing, validation, enriching, and standardization before loading the data into a destination like a data lake or data warehouse.

BI 52

BI ETL Tools Retail Healthcare 52

Monte Carlo

JANUARY 16, 2024

Data issues identified and resolved faster A bright and rapidly evolving future 1. Data lake and data warehouse convergence The data lake vs data warehouse question is constantly evolving. The maxim that data warehouses hold structured data while data lakes hold unstructured data is quickly breaking down.

Data Lake 52

Data Lake Data Warehouse Unstructured Data AWS 52

Knowledge Hut

DECEMBER 26, 2023

Business Intelligence Business Intelligence is an intrinsic element of modern business. By referring to the following books, you will learn about various BI tools and operations like creating reports, tracking performance, managing data sources, etc.

Data Science 52

Data Science Programming Language Scala R (Programming) 52

AltexSoft

AUGUST 29, 2023

In 2010, a transformative concept took root in the realm of data storage and analytics — a data lake. The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Monte Carlo

AUGUST 25, 2023

Understanding data warehouses A data warehouse is a consolidated storage unit and processing hub for your data. Teams using a data warehouse usually leverage SQL queries for analytics use cases. This same structure aids in maintaining data quality and simplifies how users interact with and understand the data.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

Knowledge Hut

JANUARY 30, 2024

Goal To extract and transform data from its raw form into a structured format for analysis. To uncover hidden knowledge and meaningful patterns in data for decision-making. Data Source Typically starts with unprocessed or poorly structured data sources. Analyzing and deriving valuable insights from data.

ETL Tools 52

ETL Tools Database-centric Data Mining Raw Data 52

Knowledge Hut

APRIL 25, 2023

With Power BI, data engineers can easily create interactive reports and dashboards that can be accessed from anywhere, on any device. Key features: Robust data visualization capabilities Seamless integration with Microsoft tools Easy-to-use interface 2. It is one of the most liked data engineering tools of the present day.

Data Engineering 52

Data Engineering Data Engineer Engineering Google Cloud 52

Monte Carlo

AUGUST 31, 2023

Data is a priority for your CEO, as it often is for digital-first companies, and she is fluent in the latest and greatest business intelligence tools. What about a frantic email from your CTO about “duplicate data” in a business intelligence dashboard? Rise of the Data Lakehouse Data warehouse or data lake?

Data Lake 52

Data Lake Data Pipeline Unstructured Data Data Warehouse 52

phData: Data Engineering

JULY 18, 2023

CDWs are designed for running large and complex queries across vast amounts of data, making them ideal for centralizing an organization’s analytical data for the purpose of business intelligence and data analytics applications. However, merely knowing what it consists of isn’t enough.

Data Warehouse 52

Data Warehouse Pipeline-centric Government Data 52

Monte Carlo

APRIL 18, 2022

Talk about a waste of time… In a decentralized data team, data team members are part of different business units. Data team members in each domain are responsible for fielding incidents for their stakeholders.

Data Pipeline 52

Data Pipeline Software Engineering Software Engineer Data Engineering 52

ProjectPro

JANUARY 31, 2022

This is the reason why we need Data Warehouses. What is Snowflake Data Warehouse? A Data Warehouse is a central information repository that enables Data Analytics and Business Intelligence (BI) activities. They can also design and run data apps and securely share, gather, and commercialize real-time data.

Architecture 52

Architecture IT Data Warehouse Amazon Web Services 52

ProjectPro

AUGUST 24, 2021

Google BigQuery receives the structured data from workers. Finally, the data is passed to Google Data studio for visualization. But, it is important to wonder how an organization will achieve the same steps on data of different types. The Yelp dataset JSON stream is published to the PubSub topic.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

Monte Carlo

JUNE 14, 2023

While it might be tempting to continue using custom code to transform your data, it does increase the chances of errors being made as the code is not easily replicable and must be rewritten every time a process takes place. Data catalog Some organizations choose to implement data catalog solutions for data governance and compliance use cases.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

AltexSoft

OCTOBER 8, 2021

The platform’s main capabilities comprise data integration, data quality assurance, and data governance. Xplenty: convenient low-code environment for data integration. The toolkit allows you to quickly build data pipelines , automate integration tasks, and monitor jobs. Source: G2. Ease of use.

Data Integration 52

Data Integration Hadoop Data Warehouse Data Lake 52

AltexSoft

JULY 29, 2022

Developed in 2006 by Doug Cutting and Mike Cafarella to run the web crawler Apache Nutch, it has become a standard for Big Data analytics. According to the study by the Business Application Research Center (BARC), Hadoop found intensive use as. a suitable technology to implement data lake architecture.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

RandomTrees

MAY 6, 2025

This new technology is helping businesses make faster marketing predictions and better manage customer interactions. However, to succeed, AI requires a foundation of reliable and structured data. Modern data engineering can help with this. Without it, AI technologies wouldn’t have access to high-quality data.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Cleanse 52