Knowledge Hut

JANUARY 18, 2024

Roles and Responsibilities Finding data sources and automating the data collection process Discovering patterns and trends by analyzing information Performing data pre-processing on both structured and unstructured data Creating predictive models and machine-learning algorithms Average Salary: USD 81,361 (1-3 years) / INR 10,00,000 per annum 3.

Data Science 98

Data Science Business Analyst ETL Method Data Architect 98

ProjectPro

JUNE 6, 2015

In an era of digital transformation of enterprises, there are several questions that have arisen- How can business intelligence provide real time insights? How can business intelligence scale and analyse the growing data heap? How can business intelligence meet changing business needs?

Business Intelligence 40

Business Intelligence Big Data BI Hadoop 40

Data Engineering Podcast

JULY 31, 2022

Summary Data lineage is the roadmap for your data platform, providing visibility into all of the dependencies for any report, machine learning model, or data warehouse table that you are working with. What is involved in integrating Manta with an organization’s data systems?

IT 100

IT Metadata MongoDB MySQL 100

Snowflake

APRIL 5, 2023

Two different data modeling approaches—dimensional data modeling and Data Vault—each have their own pros and cons. Modernizing a data warehouse with Snowflake Data Cloud is a smart investment that can provide significant benefits to businesses of all sizes, today more than ever as data models become ever more complex.

Data Warehouse 52

Data Warehouse Healthcare Unstructured Data Metadata 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

Cloud Academy

NOVEMBER 6, 2022

A Headless BI tool is a set of components that acts as middleware between your data warehouse and your business intelligence applications. It provides us with four main data-related components without the need of designing and implementing custom solutions. This is the responsibility of the BI tool.

Cloud 52

Cloud BI Data Warehouse Business Intelligence 52

ProjectPro

JULY 22, 2016

For the same cost, organizations can now store 50 times as much data as in a Hadoop data lake than in a data warehouse. Data lake is gaining momentum across various organizations and everyone wants to know how to implement a data lake and why.

Data Lake 40

Data Lake Data Warehouse Hadoop Unstructured Data 40

Knowledge Hut

DECEMBER 26, 2023

Thus, to build a career in Data Science, you need to be familiar with how the business operates, its business model, strategies, problems, and challenges. Data Science Roles As Data Science is a broad field, you will find multiple different roles with different responsibilities.

Data Science 97

Data Science BI Business Intelligence Data Mining 97

Knowledge Hut

DECEMBER 29, 2023

Big Data is a part of this umbrella term, which encompasses Data Warehousing and Business Intelligence as well. A Data Engineer's primary responsibility is the construction and upkeep of a data warehouse. They construct pipelines to collect and transform data from many sources.

Data Science 98

Data Science Data Mining Deep Learning Programming Language 98

Silectis

JANUARY 3, 2021

If you’re new to data engineering or are a practitioner of a related field, such as data science, or business intelligence, we thought it might be helpful to have a handy list of commonly used terms available for you to get up to speed. Big Data Large volumes of structured or unstructured data.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

phData: Data Engineering

SEPTEMBER 19, 2023

With the amount of data companies are using growing to unprecedented levels, organizations are grappling with the challenge of efficiently managing and deriving insights from these vast volumes of structured and unstructured data. FAQs What’s the difference between a data lake and a data warehouse?

Data Lake 52

Data Lake Process Metadata Data Warehouse 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?

Data Lake 52

Data Lake Data Pipeline Unstructured Data Data Warehouse 52

Knowledge Hut

JUNE 23, 2023

You must develop predictive models to help industries and businesses make data-driven decisions. Steps to Become a Data Engineer One excellent point is that you don’t need to enter the industry as a data engineer. Data warehousing to aggregate unstructured data collected from multiple sources.

Data Engineering 52

Data Engineering Data Engineer Engineering Non-relational Database 52

Ascend.io

AUGUST 31, 2023

Secondly , the rise of data lakes that catalyzed the transition from ELT to ELT and paved the way for niche paradigms such as Reverse ETL and Zero-ETL. Still, these methods have been overshadowed by EtLT — the predominant approach reshaping today’s data landscape.

Data Lake 52

Data Lake ETL Tools Data Warehouse Data Pipeline 52

Rockset

JULY 29, 2022

Enterprise data warehouses (EDWs) became necessary in the 1980s when organizations shifted from using data for operational decisions to using data to fuel critical business decisions. Data warehouses are popular because they help break down data silos and ensure data consistency.

Data Analytics 52

Data Analytics Data Warehouse Datasets Google Cloud 52

Knowledge Hut

JUNE 20, 2023

Skills A data engineer should have good programming and analytical skills with big data knowledge. Examples Pull daily tweets from the data warehouse hive spreading in multiple clusters. They transform unstructured data into scalable models for data science.

Machine Learning 52

Machine Learning Data Engineering Data Engineer Engineering 52

Monte Carlo

JUNE 28, 2022

By design, data was less structured with limited metadata and no ACID properties. As a result, data observability has become particularly important for data lake environments as they often hold large amounts of unstructured data, making data quality issues challenging to detect, resolve, and prevent.

Data Lake 52

Data Lake Metadata AWS Data Warehouse 52

Knowledge Hut

JANUARY 30, 2024

This data can be structured, semi-structured, or entirely unstructured, making it a versatile tool for collecting information from various origins. The extracted data is then duplicated or transferred to a designated destination, often a data warehouse optimized for Online Analytical Processing (OLAP).

ETL Tools 52

ETL Tools Database-centric Data Mining Data Cleanse 52

ProjectPro

DECEMBER 7, 2021

In broader terms, two types of data -- structured and unstructured data -- flow through a data pipeline. The structured data comprises data that can be saved and retrieved in a fixed format, like email addresses, locations, or phone numbers. What is a Big Data Pipeline?

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

AltexSoft

FEBRUARY 11, 2023

It serves as a foundation for the entire data management strategy and consists of multiple components including data pipelines; , on-premises and cloud storage facilities – data lakes , data warehouses , data hubs ;, data streaming and Big Data analytics solutions ( Hadoop , Spark , Kafka , etc.);

Data Architect 87

Data Architect Certification Generalist Big Data 87

Monte Carlo

AUGUST 25, 2023

Different vendors offering data warehouses, data lakes, and now data lakehouses all offer their own distinct advantages and disadvantages for data teams to consider. So let’s get to the bottom of the big question: what kind of data storage layer will provide the strongest foundation for your data platform?

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

Snowflake

NOVEMBER 2, 2023

Over the years, the technology landscape for data management has given rise to various architecture patterns, each thoughtfully designed to cater to specific use cases and requirements. These patterns include both centralized storage patterns like data warehouse , data lake and data lakehouse , and distributed patterns such as data mesh.

Data Lake 91

Data Lake Data Warehouse Cloud Unstructured Data 91

Knowledge Hut

JUNE 26, 2023

Using big data, we are able to transform unstructured data, such as customer reviews, into actionable insights, which enables businesses to better understand how and why customers prefer their products or services and to make improvements to their operations as quickly as is practically possible.

Data Engineering 98

Data Engineering Data Engineer Coding Project 98

Databand.ai

DECEMBER 13, 2022

We’d be remiss not to share that Joseph was a recent guest on Databand’s MAD Data Podcast , where he discussed ways to keep data systems from becoming unwieldy and shared tips for data teams to manage their data warehouses and keep data pipelines running reliably. You can also watch the video recording.

Data Engineering 52

Data Engineering Data Engineer Engineering AWS 52

ProjectPro

SEPTEMBER 30, 2014

While the initial era of ETL ignited enough sparks and got everyone to sit up, take notice and applaud its capabilities, its usability in the era of Big Data is increasingly coming under the scanner as the CIOs start taking note of its limitations.

Hadoop 52

Hadoop Unstructured Data ETL Tools ETL System 52

U-Next

SEPTEMBER 7, 2022

The terms “ Data Warehouse ” and “ Data Lake ” may have confused you, and you have some questions. Structuring data refers to converting unstructured data into tables and defining data types and relationships based on a schema. What is Data Warehouse? .

Data Lake 52

Data Lake Data Warehouse Unstructured Data Amazon Web Services 52

Data Engineering Podcast

JUNE 26, 2022

Push information about data freshness and quality to your business intelligence, automatically scale up and down your warehouse based on usage patterns, and let the bots answer those questions in Slack so that the humans can focus on delivering real value. Unstruk is the DataOps platform for your unstructured data.

Datasets 130

Datasets Unstructured Data Metadata MongoDB 130

ProjectPro

SEPTEMBER 11, 2015

For professionals from BI background, learning Hadoop is necessary because with data explosion it is becoming difficult for traditional databases to store unstructured data. Hadoop still has a long way to go when it comes to presenting clean and readable data solutions. Hadoop is not suitable for all kinds of data.

Hadoop 52

Hadoop Java BI Big Data 52

Monte Carlo

JANUARY 16, 2024

At the center of it all is the data warehouse, the lynchpin of any modern data stack. In this blog post, we’ll look at six innovations that are shaping the future of the data warehousing, as well as challenges and considerations that organizations should keep in mind. Data lake and data warehouse convergence 2.

Data Lake 52

Data Lake Data Warehouse Unstructured Data AWS 52

ProjectPro

JUNE 9, 2021

Data Science looks into boosting the performance of a machine learning model. Data Engineering handles the entire data pipeline's optimization and efficiency for sourcing data from the data warehouse. It entails generating data visualizations and charts for analysis. Who is a Data Scientist?

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 52

Monte Carlo

FEBRUARY 15, 2023

Cloud data warehouses solve these problems. Belonging to the category of OLAP (online analytical processing) databases, popular data warehouses like Snowflake, Redshift and Big Query can query one billion rows in less than a minute. What is a data warehouse?

Data Warehouse 52

Data Warehouse Unstructured Data AWS Business Intelligence 52

Data Engineering Podcast

NOVEMBER 27, 2022

Push information about data freshness and quality to your business intelligence, automatically scale up and down your warehouse based on usage patterns, and let the bots answer those questions in Slack so that the humans can focus on delivering real value. images, documents, etc.)

Data Process 100

Data Process Process Metadata Business Intelligence 100

Knowledge Hut

DECEMBER 26, 2023

Let us first get a clear understanding of why Data Science is important. What is the need for Data Science? If we look at history, the data that was generated earlier was primarily structured and small in its outlook. A simple usage of Business Intelligence (BI) would be enough to analyze such datasets.

Data Engineering 98

Data Engineering Data Engineer Engineering Pipeline-centric 98

Towards Data Science

DECEMBER 4, 2023

A robust data infrastructure is a must-have to compete in the F1 business. We’ll build a data architecture to support our racing team starting from the three canonical layers : Data Lake, Data Warehouse, and Data Mart. Data Marts There is a thin line between Data Warehouses and Data Marts.

Data Engineering 87

Data Engineering Data Engineer Engineering Data Lake 87

Monte Carlo

JUNE 14, 2023

The modern data stack era , roughly 2017 to present data, saw the widespread adoption of cloud computing and modern data repositories that decoupled storage from compute such as data warehouses, data lakes, and data lakehouses.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

Edureka

FEBRUARY 9, 2023

Ingest data into one or more Azure services, including Azure Data Lake, Azure Storage, Azure SQL, and Azure DW, and process the data in Azure Databricks. Develop pipelines in ADF that extract, transform, and load data from sources such as Azure SQL, Blob storage, Azure SQL Data Warehouse, write-back tools, and others.

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 52

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