The Modern Data Company

JANUARY 22, 2024

The Modern Story: Navigating Complexity and Rethinking Data in The Business Landscape Enterprises face a data landscape marked by the proliferation of IoT-generated data, an influx of unstructured data, and a pervasive need for comprehensive data analytics.

Data Integration 87

Data Integration Metadata Government Unstructured Data 87

Sync Computing

NOVEMBER 7, 2024

Despite these limitations, data warehouses, introduced in the late 1980s based on ideas developed even earlier, remain in widespread use today for certain business intelligence and data analysis applications. While data warehouses are still in use, they are limited in use-cases as they only support structured data.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Unstructured Data 52

Monte Carlo

JANUARY 5, 2024

This architecture format consists of several key layers that are essential to helping an organization run fast analytics on structured and unstructured data. Table of Contents What is data lakehouse architecture? The 5 key layers of data lakehouse architecture 1. Metadata layer 4. This starts at the data source.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

Monte Carlo

JANUARY 5, 2024

This architecture format consists of several key layers that are essential to helping an organization run fast analytics on structured and unstructured data. Table of Contents What is data lakehouse architecture? The 5 key layers of data lakehouse architecture 1. Metadata layer 4. This starts at the data source.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 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

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

Edureka

MAY 8, 2025

A lot of people who work in ETL/DWH call this the “Landing Zone of data.” ” We are only now looking at ALL kinds of information, regardless of its structure, building, metadata, etc. One idea behind Data Lake is that technology has now made it possible for a company to store ALL the data it creates or gets.

Data Lake 52

Data Lake Data Warehouse Raw Data Datasets 52

Monte Carlo

AUGUST 25, 2023

One advantage of data warehouses is their integrated nature. As fully managed solutions, data warehouses are designed to offer ease of construction and operation. A warehouse can be a one-stop solution, where metadata, storage, and compute components come from the same place and are under the orchestration of a single vendor.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

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

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

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

JUNE 28, 2022

Traditionally, data lakes held raw data in its native format and were known for their flexibility, speed, and open source ecosystem. By design, data was less structured with limited metadata and no ACID properties. Unity Catalog The Unity Catalog unifies metastores, catalogs, and metadata within Databricks.

Data Lake 52

Data Lake Metadata AWS Data Warehouse 52

Knowledge Hut

MAY 9, 2024

Data Architect ScyllaDB Data architects play a crucial role in designing an organization's data management framework by assessing data sources and integrating them into a centralized plan. Average Annual Salary of Data Modeler A data modeler can earn $126,811 annually.

Data Science 52

Data Science Data Architect Data Mining Programming Language 52

Snowflake

APRIL 5, 2023

With Snowflake’s support for multiple data models such as dimensional data modeling and Data Vault, as well as support for a variety of data types including semi-structured and unstructured data, organizations can accommodate a variety of sources to support their different business use cases.

Data Warehouse 52

Data Warehouse Healthcare Unstructured Data Metadata 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. Now, let’s take a look at each in a bit more detail.

Data Lake 52

Data Lake Architecture Business Intelligence Unstructured Data 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. Want to learn more about data governance? Check out our Data Governance on Snowflake blog!

Data Lake 52

Data Lake Process Metadata Data Warehouse 52

Monte Carlo

JANUARY 23, 2024

Gen AI can whip up serviceable code in moments — making it much faster to build and test data pipelines. Today’s LLMs can already process enormous amounts of unstructured data, automating much of the monotonous work of data science. But what does that mean for the roles of data engineers and data scientists going forward?

Database-centric 52

Database-centric Pipeline-centric Metadata Unstructured Data 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 Machine Learning 52

Precisely

OCTOBER 10, 2024

Organizations are evaluating modern data management architectures that will support wider data democratization. Why data democratization matters First and foremost, data democratization is about empowering employees to access the data that informs better business decisions.

Data Governance 52

Data Governance Government Data Unstructured Data 52

Monte Carlo

AUGUST 19, 2023

We’ll cover: What is a data platform? Amazon S3 – An object storage service for structured and unstructured data, S3 gives you the compute resources to build a data lake from scratch. Data ingestion tools, like Fivetran, make it easy for data engineering teams to port data to their warehouse or lake.

Building 52

Building BI Data Lake Data Governance 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

AltexSoft

MARCH 30, 2023

This way, Delta Lake brings warehouse features to cloud object storage — an architecture for handling large amounts of unstructured data in the cloud. Source: The Data Team’s Guide to the Databricks Lakehouse Platform Integrating with Apache Spark and other analytics engines, Delta Lake supports both batch and stream data processing.

Scala 64

Scala Data Lake Machine Learning BI 64

Monte Carlo

JUNE 14, 2023

Amazon S3 – An object storage service for structured and unstructured data, S3 gives you the compute resources to build a data lake from scratch. Sigma Computing – A BI platform that delivers cloud-scale analytics with the simplicity of a spreadsheet and familiar data visualizations.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

AltexSoft

DECEMBER 23, 2022

One of the main reasons behind this is the need to timely process huge volumes of data in any format. As said, ETL and ELT are two approaches to moving and manipulating data from various sources for business intelligence. In ETL, all the transformations are done before the data is loaded into a destination system.

Process 52

Process Building Raw Data Data Lake 52

ProjectPro

JANUARY 31, 2023

Big data enables businesses to get valuable insights into their products or services. Almost every company employs data models and big data technologies to improve its techniques and marketing campaigns. Most leading companies use big data analytical tools to enhance business decisions and increase revenues.

Big Data 52

Big Data Hadoop Relational Database AWS 52

ProjectPro

SEPTEMBER 26, 2021

Becoming a Big Data Engineer - The Next Steps Big Data Engineer - The Market Demand An organization’s data science capabilities require data warehousing and mining, modeling, data infrastructure, and metadata management. Most of these are performed by Data Engineers.

Big Data 52

Big Data Data Engineering Data Engineer Engineering 52

Ascend.io

AUGUST 31, 2023

The Data Warehouse Pattern The heart of a data warehouse lies in its schema, capturing intricate details of business operations. This unchanging schema forms the foundation for all queries and business intelligence. Modern platforms like Redshift , Snowflake , and BigQuery have elevated the data warehouse model.

Data Lake 52

Data Lake Data Warehouse ETL Tools Data Pipeline 52

AltexSoft

OCTOBER 3, 2022

They allow for representing various types of data and content (data schema, taxonomies, vocabularies, and metadata) and making them understandable for computing systems. There are numerous applications of knowledge graphs both in research and industry as they are one of the best and most flexible ways to represent data.

Relational Database 52

Relational Database Banking Media Computer Science 52

Knowledge Hut

JULY 26, 2023

Columnar databases, by organizing data by columns, can efficiently compress and store comparable data together, resulting in higher compression ratios and faster data scans. They are commonly used in applications such as data warehousing, business intelligence, and analytics.

Database 52

Database NoSQL MongoDB Telecommunication 52

AltexSoft

SEPTEMBER 21, 2023

These indices are specially designed data structures that map out the data for rapid searches, allowing for the retrieval of queries in milliseconds. As a result, Elasticsearch is exceptionally efficient in managing structured and unstructured data. Framework Programming The Good and the Bad of Node.js

Engineering 52

Engineering NoSQL Programming Language Java 52

ProjectPro

AUGUST 24, 2021

Thus, as a learner, your goal should be to work on projects that help you explore structured and unstructured data in different formats. Data Warehousing: Data warehousing utilizes and builds a warehouse for storing data. A data engineer interacts with this warehouse almost on an everyday basis.

Data Engineering 52

Data Engineering Data Engineer Coding Project 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. Let’s see why.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

ProjectPro

OCTOBER 20, 2021

We need to understand and monitor the current state of data evolution at the enterprise level. This happens with the help of Business Intelligence Tools, analytics, and reporting. Multiple hypotheses and use-cases are put forth that are attempted to solve with the Data Science. Discuss a few use cases.

Machine Learning 52

Machine Learning Algorithm Data Science Government 52

ProjectPro

APRIL 4, 2015

” Big Data Hadoop Interview Questions Hadoop interviewers don’t bother with syntax questions or other simple hadoop interview questions that can be easily answered with the help of Google. Random Job Distribution Coordinate resource management Self managed resources and worker Process Structured and Semi-Structured Data.

Hadoop 40

Hadoop Big Data Recruitment Java 40

ProjectPro

NOVEMBER 30, 2021

ETL (Extract, Transform, and Load) Pipeline involves data extraction from multiple sources like transaction databases, APIs, or other business systems, transforming it, and loading it into a cloud-hosted database or a cloud data warehouse for deeper analytics and business intelligence.

Process 52

Process Data Warehouse Data Pipeline AWS 52