ProjectPro

JUNE 6, 2025

However, the modern data ecosystem encompasses a mix of unstructured and semi-structured data—spanning text, images, videos, IoT streams, and more—these legacy systems fall short in terms of scalability, flexibility, and cost efficiency. That’s where data lakes come in.

Data Lake

ProjectPro

JUNE 6, 2025

The source function, on the other hand, is used to reference external data sources that are not built or transformed by DBT itself but are brought into the DBT project from external systems, such as raw data in a data warehouse. Imagine your organization has a mix of structured and semi-structured data.

Raw Data

ProjectPro

JUNE 6, 2025

Features of Snowflake Highly Scalable- Users can establish an almost infinite range of virtual warehouses, each of which runs its task using the data in its database. A data engineer is an individual who builds, maintains, and optimizes data infrastructure for data acquisition, storage, processing, and access.

Data Engineering

Teradata

MAY 30, 2025

Setting up the dbt project dbt (data build tool) allows you to transform your data by writing, documenting, and executing SQL workflows. The sample dbt project included converts raw data from an app database into a dimensional model, preparing customer and purchase data for analytics. dbt-core dagster==1.7.9

Data Integration

ProjectPro

JUNE 6, 2025

You have probably heard the saying, "data is the new oil". It is extremely important for businesses to process data correctly since the volume and complexity of raw data are rapidly growing. However, the vast volume of data will overwhelm you if you start looking at historical trends. Well, it surely is!

BI

Monte Carlo

NOVEMBER 21, 2024

You get the structure and performance of a warehouse with the flexibility and scalability of a lake. Want to run SQL queries on your structured data while also keeping raw files for your data scientists to play with? The data lakehouse has got you covered!

Data Pipeline

ProjectPro

JUNE 6, 2025

Did you know AWS S3 allows you to scale storage resources to meet evolving needs with a data durability of 99.999999999%? Data scientists and developers can upload raw data, such as images, text, and structured information, to S3 buckets. Don't be afraid of data Science!

Machine Learning

ProjectPro

JUNE 6, 2025

Building data pipelines is a core skill for data engineers and data scientists as it helps them transform raw data into actionable insights. You’ll walk through each stage of the data processing workflow, similar to what’s used in production-grade systems. b64encode(creds.encode()).decode()

Python

ProjectPro

JUNE 6, 2025

Your SQL skills as a data engineer are crucial for data modeling and analytics tasks. Making data accessible for querying is a common task for data engineers. Collecting the raw data, cleaning it, modeling it, and letting their end users access the clean data are all part of this process.

Data Engineering

ProjectPro

JUNE 6, 2025

Insurance Data List of documents required for processing auto insurance requests. Client's Raw data A document explaining the reason for the customer's request. This data gathered by the Data Engineer is then used further in the data analysis process by Data Analysts and Data Scientists.

Data Analysis

ProjectPro

JUNE 6, 2025

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. Step 1- Automating the Lakehouse's data intake.

Data Pipeline

RudderStack

JUNE 24, 2025

Data lakes physically store raw data in a central repository, while data federation provides virtual access to distributed data without moving it, offering different trade-offs in performance, storage requirements, and real-time capabilities. Can data federation work with both structured and unstructured data?

IT

ProjectPro

JUNE 6, 2025

This means that a data warehouse is a collection of technologies and components that are used to store data for some strategic use. Data is collected and stored in data warehouses from multiple sources to provide insights into business data. Data from data warehouses is queried using SQL.

Data Lake

ProjectPro

JUNE 6, 2025

As highlighted by McKinsey, organizations fueled by data are 23 times more likely to acquire customers, six times as likely to retain them, and a staggering 19 times more likely to be profitable. Yet, the journey from raw data to actionable insights is complex, requiring meticulous organization and structure for sustained success.

Data Science

ProjectPro

JUNE 6, 2025

Combining concepts of conciseness and functional paradigm with OOP and high-level performance, data engineers can use Scala equally for lightweight, user-facing applications and terabyte-level big data pipelines with Spark jobs and distributed systems.

Scala

ProjectPro

JUNE 6, 2025

Provides Powerful Computing Resources for Data Processing Before inputting data into advanced machine learning models and deep learning tools, data scientists require sufficient computing resources to analyze and prepare it. Additionally, Snowflake is batch-based and requires the complete dataset for results computation.

Architecture

ProjectPro

JUNE 6, 2025

Azure Data Factory Source- Microsoft Azure Data Factory is a cloud-based real-time data integration service that simplifies the process of building and managing data pipelines for moving and transforming data between Azure services and on-premises data sources.

Data Ingestion

ProjectPro

JUNE 6, 2025

Data Science Pipeline Workflow The data science pipeline is a structured framework for extracting valuable insights from raw data and guiding analysts through interconnected stages. This phase demands meticulous attention to detail to acquire high-quality and relevant data.

Data Science

ProjectPro

JUNE 6, 2025

Here's an example of a job description of an ETL Data Engineer below: Source: www.tealhq.com/resume-example/etl-data-engineer Key Responsibilities of an ETL Data Engineer Extract raw data from various sources while ensuring minimal impact on source system performance.

Data Engineering

ProjectPro

JUNE 6, 2025

To extract data, you typically need to set up an API connection (an interface to get the data from its sources), transform it, clean it up, convert it to another format, map similar records to one another, validate the data, and then put it into a database (e.g. Let us understand how a simple ETL pipeline works.

Data Science

Scott Logic

JULY 7, 2025

Specific use cases include: Risk Identification : Deal teams can move beyond reviewing audited financials by using raw data to independently assess financial health. Data can be compared against sector benchmarks to spot anomalies in key ratios or trends. Firms are also increasingly using analytics and AI in deal origination.

Portfolio

Edureka

MAY 27, 2025

Synapse Data Warehouse Fabric’s enterprise-class data warehouse facilitates deep integration with OneLake, distributed processing, and massive parallelism. For workloads involving structured data, it offers governed SQL-based analytics with excellent performance. Transform Your Data Analytics with Microsoft Fabric!

Architecture

ProjectPro

JUNE 6, 2025

Data engineers leverage AWS Glue's capability to offer all features, from data extraction through transformation into a standard Schema. AWS Redshift Amazon Redshift offers petabytes of structured or semi-structured data storage as an ideal data warehouse option.

AWS

ProjectPro

JUNE 6, 2025

Today, businesses use traditional data warehouses to centralize massive amounts of raw data from business operations. Amazon Redshift is helping over 10000 customers with its unique features and data analytics properties. Amazon Redshift is a cloud data warehouse that stores structured and semi-structured data.

Data Pipeline

ProjectPro

JUNE 6, 2025

Wordsmith is a report-writing tool that can use structured data and LLMs to generate written summaries in plain language, perfect for business executives who prefer high-level insights. Real-Time Data Monitoring Agents These agents monitor data in real-time, providing immediate feedback or alerts based on the analysis.

Data Analysis

ProjectPro

JUNE 6, 2025

Most of us have observed that data scientist is usually labeled the hottest job of the 21st century, but is it the only most desirable job? No, that is not the only job in the data world. by ingesting raw data into a cloud storage solution like AWS S3. Use the ESPNcricinfo Ball-by-Ball Dataset to process match data.

Data Engineering

ProjectPro

JUNE 6, 2025

Big data operations require specialized tools and techniques since a relational database cannot manage such a large amount of data. Big data enables businesses to gain a deeper understanding of their industry and helps them extract valuable information from the unstructured and raw data that is regularly collected.

Big Data

ProjectPro

JUNE 6, 2025

Pandas Pandas is a popular Python data manipulation library often used for data extraction and transformation in ETL processes. It provides data structures and functions for working with structured data, making it an excellent choice for data preprocessing.

Python

ProjectPro

JUNE 6, 2025

Data Engineers usually opt for database management systems for database management and their popular choices are MySQL, Oracle Database, Microsoft SQL Server, etc. When working with real-world data, it may only sometimes be the case that the information is stored in rows and columns.

Data Engineering

ProjectPro

JUNE 6, 2025

Identifying patterns is one of the key purposes of statistical data analysis. For instance, it can be helpful in the retail industry to find patterns in unstructured and semi-structured data to help make more effective decisions to improve the customer experience.

Data Analysis Tools

ProjectPro

JUNE 6, 2025

When you create an index, the data and embeddings are stored in a structured format. Persisting these indexes saves them to a storage medium (local storage or database) for reuse without reprocessing raw data every time. and return them in a consistent, structured format regardless of the source format.

Building

ProjectPro

JUNE 6, 2025

This explosive growth in online content has made web scraping essential for gathering data, but traditional scraping methods face limitations in handling unstructured information. Web scraping typically extracts raw data, which often requires manual cleaning and processing.

Project

ProjectPro

JUNE 6, 2025

Through their ability to bridge the gap between raw data and computational processes, vector embeddings have become indispensable tools, transforming the landscape of data-driven decision-making and advancing the frontiers of AI. It seamlessly scales to manage vast amounts of data objects, supporting billions of entries.

Database

ProjectPro

JUNE 6, 2025

We'll walk you through creating a Python dashboard step by step, demonstrating how to leverage interactivity and real-time data visualization updates effectively. Get ready to turn raw data into insightful, interactive dashboards! def create_connection(): try: # create connection with data warehouse conn = sqlite3.connect('/content/data_warehouse.db')

Python

ProjectPro

JUNE 6, 2025

Relational Database Management Systems (RDBMS) Non-relational Database Management Systems Relational Databases primarily work with structured data using SQL (Structured Query Language). SQL works on data arranged in a predefined schema. Non-relational databases support dynamic schema for unstructured data.

Data Engineering

Edureka

APRIL 14, 2025

Microsoft offers a leading solution for business intelligence (BI) and data visualization through this platform. It empowers users to build dynamic dashboards and reports, transforming raw data into actionable insights. However, it leans more toward transforming and presenting cleaned data rather than processing raw datasets.

BI

ProjectPro

JUNE 6, 2025

Big data technologies used: Microsoft Azure, Azure Data Factory, Azure Databricks , Spark Big Data Architecture: This sample Hadoop real-time project starts off by creating a resource group in azure. To this group, we add a storage account and move the raw data. Repository Link: [link] 34.

Hadoop