Monte Carlo

FEBRUARY 15, 2023

A data warehouse is an online analytical processing system that stores vast amounts of data collected within a company’s ecosystem and acts as a single source of truth to enable downstream data consumers to perform business intelligence tasks, machine learning modeling, and more. They need to be transformed.

Data Warehouse 52

Data Warehouse Unstructured Data AWS Business Intelligence 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

AltexSoft

JANUARY 26, 2022

An analytics engineer role is a blurry line between technology and business, so the responsibilities may differ greatly from company to company. Below we list the core duties that this data specialist may undertake. Data modeling. Transformations may include. splitting a single column into multiple ones, to name a few.

Engineering 59

Engineering Software Engineer Software Engineering Data Warehouse 59

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

ProjectPro

DECEMBER 7, 2021

Keeping data in data warehouses or data lakes helps companies centralize the data for several data-driven initiatives. While data warehouses contain transformed data, data lakes contain unfiltered and unorganized raw data.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

ProjectPro

SEPTEMBER 26, 2021

Basic knowledge of ML technologies and algorithms will enable you to collaborate with the engineering teams and the Data Scientists. It will also assist you in building more effective data pipelines. It then loads the transformed data in the database or other BI platforms for use. Don’t Mention Every Tool.

Big Data 52

Big Data Data Engineering Data Engineer Engineering 52

AltexSoft

SEPTEMBER 10, 2021

So, why does anyone need to integrate data in the first place? Today, companies want their business decisions to be driven by data. But here’s the thing — information required for business intelligence (BI) and analytics processes often lives in a breadth of databases and applications.

Data Integration 52

Data Integration Data Consolidation Data Warehouse BI 52

ProjectPro

JULY 27, 2021

Data engineers and data scientists work very closely together, but there are some differences in their roles and responsibilities. Data Engineer Data scientist The primary role is to design and implement highly maintainable database management systems. What is the best way to capture streaming data in Azure?

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

Striim

JANUARY 8, 2025

A 2023 Salesforce study revealed that 80% of business leaders consider data essential for decision-making. However, a Seagate report found that 68% of available enterprise data goes unleveraged, signaling significant untapped potential for operational analytics to transform raw data into actionable insights.

BI 52

BI Business Analyst Retail Raw Data 52

ProjectPro

NOVEMBER 30, 2021

Now that we have understood how much significant role data plays, it opens the way to a set of more questions like How do we acquire or extract raw data from the source? How do we transform this data to get valuable insights from it? Where do we finally store or load the transformed data?

Process 52

Process Data Warehouse Data Pipeline AWS 52