Monte Carlo

JANUARY 5, 2024

Data lakehouse architecture combines the benefits of data warehouses and data lakes, bringing together the structure and performance of a data warehouse with the flexibility of a data lake. The data lakehouse’s semantic layer also helps to simplify and open data access in an organization.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

Monte Carlo

JANUARY 5, 2024

Data lakehouse architecture combines the benefits of data warehouses and data lakes, bringing together the structure and performance of a data warehouse with the flexibility of a data lake. The data lakehouse’s semantic layer also helps to simplify and open data access in an organization.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

Monte Carlo

FEBRUARY 15, 2023

On the surface, the promise of scaling storage and processing is readily available for databases hosted on AWS RDS, GCP cloud SQL and Azure to handle these new workloads. Cloud data warehouses solve these problems. What is a data warehouse? Let’s imagine a scenario where you’re collecting orders information.

Data Warehouse 52

Data Warehouse Unstructured Data AWS Business Intelligence 52

Data Engineering Podcast

JUNE 12, 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.

Metadata 100

Metadata Unstructured Data Business Intelligence MongoDB 100

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

Certain roles like Data Scientists require a good knowledge of coding compared to other roles. Data Science also requires applying Machine Learning algorithms, which is why some knowledge of programming languages like Python, SQL, R, Java, or C/C++ is also required.

Data Science 97

Data Science BI Business Intelligence Data Mining 97

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

Monte Carlo

JULY 19, 2023

In this article, we’ll present you with the Five Layer Data Stack—a model for platform development consisting of five critical tools that will not only allow you to maximize impact but empower you to grow with the needs of your organization. Before you can model the data for your stakeholders, you need a place to collect and store it.

Building 96

Building Business Intelligence Cloud Storage BI 96

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

JULY 21, 2023

In this article, we’ll present you with the Five Layer Data Stack — a model for platform development consisting of five critical tools that will not only allow you to maximize impact but empower you to grow with the needs of your organization. Before you can model the data for your stakeholders, you need a place to collect and store it.

Building 76

Building Business Intelligence BI Cloud Storage 76

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

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

Monte Carlo

JANUARY 27, 2024

In this article, we’ll present you with the Five Layer Modern Data Stack—a model for platform development consisting of five critical tools that will not only allow you to maximize impact but empower you to grow with the needs of your organization. Before you can model the data for your stakeholders, you need a place to collect and store it.

Building 52

Building Business Intelligence Cloud Storage BI 52

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

Knowledge Hut

JUNE 23, 2023

You should have the expertise to collect data, conduct research, create models, and identify patterns. You should be well-versed with SQL Server, Oracle DB, MySQL, Excel, or any other data storing or processing software. You must develop predictive models to help industries and businesses make data-driven decisions.

Data Engineering 52

Data Engineering Data Engineer Engineering Non-relational Database 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 19, 2023

We’ll cover: What is a data platform? With companies moving their data platforms to the cloud, the emergence of cloud-native solutions ( data warehouse vs data lake or even a data lakehouse ) have taken over the market, offering more accessible and affordable options for storing data relative to many on-premises solutions.

Building 52

Building BI Data Lake Data Governance 52

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

Edureka

FEBRUARY 9, 2023

Data Ingestion and Transformation: Candidates should have experience with data ingestion techniques, such as bulk and incremental loading, as well as experience with data transformation using Azure Data Factory. SQL is also an essential skill for Azure Data Engineers.

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 52

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

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. Avro and Parquet File Formats Avro and Parquet are file formats commonly used in data lakes.

Data Lake 52

Data Lake Process Metadata Data Warehouse 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

Edureka

AUGUST 2, 2023

Data processing analysts are experts in data who have a special combination of technical abilities and subject-matter expertise. They are essential to the data lifecycle because they take unstructured data and turn it into something that can be used.

Data Process 52

Data Process Process Data Cleanse Data Mining 52

AltexSoft

NOVEMBER 10, 2021

The pun being obvious, there’s more to that than just a new term: Data lakehouses combine the best features of both data lakes and data warehouses and this post will explain this all. What is a data lakehouse? Data warehouse vs data lake vs data lakehouse: What’s the difference.

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 BI Google Cloud 64

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

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

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

AltexSoft

DECEMBER 23, 2022

It is a data integration process with which you first extract raw information (in its original formats) from various sources and load it straight into a central repository such as a cloud data warehouse , a data lake , or a data lakehouse where you transform it into suitable formats for further analysis and reporting.

Process 52

Process Building Raw Data Data Lake 52

ProjectPro

JANUARY 24, 2023

With the global cloud data warehousing market likely to be worth $10.42 billion by 2026, cloud data warehousing is now more critical than ever. Cloud data warehouses offer significant benefits to organizations, including faster real-time insights, higher scalability, and lower overhead expenses.

Bytes 52

Bytes Google Cloud Data Warehouse Datasets 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 20, 2023

He researches, develops, and implements artificial intelligence (AI) systems to automate predictive models. Languages Python, SQL, Java, Scala R, C++, Java Script, and Python Tools Kafka, Tableau, Snowflake, etc. Skills A data engineer should have good programming and analytical skills with big data knowledge.

Machine Learning 52

Machine Learning Data Engineering Data Engineer Engineering 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

ProjectPro

NOVEMBER 30, 2021

In extract-transform-load (ETL), data is obtained from multiple sources, transformed, and stored in a single data warehouse, with access to data analysts , data scientists , and business analysts for data visualization and statistical analysis model building, forecasting, etc.

Process 52

Process Data Pipeline Data Warehouse AWS 52

Monte Carlo

JANUARY 23, 2023

In this post we compare and contrast the data mesh vs data lake to illustrate the benefits of each and help discover what’s right for your data platform. In a self-service data landscape, every team wants their business intelligence served up hot and fast. Image courtesy of Monte Carlo Data.)

Data Lake 52

Data Lake Business Intelligence Unstructured Data Architecture 52

Knowledge Hut

SEPTEMBER 26, 2023

Microsoft Azure's Azure Synapse, formerly known as Azure SQL Data Warehouse, is a complete analytics offering. Designed to tackle the challenges of modern data management and analytics, Azure Synapse brings together the worlds of big data and data warehousing into a unified and seamlessly integrated platform.

Data Lake 52

Data Lake Database-centric Pipeline-centric Machine Learning 52

Monte Carlo

JUNE 26, 2023

billion in cumulative rewards to its users— Head of Data Jeff Hepburn and his team rely on Monte Carlo to deliver end-to-end visibility into the health of their data pipelines from ingestion in Databricks right down to the business intelligence layer. At Ibotta—a cash back rewards platform that has delivered more than $1.1

Data Lake 52

Data Lake Metadata Bytes Google Cloud 52