ProjectPro

JUNE 6, 2025

But this data is not that easy to manage since a lot of the data that we produce today is unstructured. In fact, 95% of organizations acknowledge the need to manage unstructured raw data since it is challenging and expensive to manage and analyze, which makes it a major concern for most businesses.

AWS 66

AWS Scala Metadata Data Lake 66

ProjectPro

JUNE 6, 2025

TensorFlow) Strong communication and presentation skills Data Scientist Salary According to the Payscale, Data Scientists earn an average of $97,680. Data Analysts Roles and Responsibilities The day-to-day job description of a data analyst is as follows: Conduct surveys to collect raw data.

Data Science 56

Data Science Data Mining Data Architect BI 56

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 40

Python Building PostgreSQL Data Pipeline 40

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. Well, it surely is!

BI 45

BI ETL Tools Retail Healthcare 45

ProjectPro

JUNE 6, 2025

This influx of data and surging demand for fast-moving analytics has had more companies find ways to store and process data efficiently. This is where Data Engineers shine! Common data sources include spreadsheets, databases, JSON data from APIs, Log files, and CSV files. Agent - Is a running JVM.

Data Ingestion 45

Data Ingestion Data Engineer Data Engineering Project 45

Snowflake

NOVEMBER 27, 2023

Hum’s fast data store is built on Elasticsearch. Snowflake’s relational database, especially when paired with Snowpark , enables much quicker use of data for ML model training and testing. Snowflake Secure Data Sharing helps reinforce the fact that our customers’ data is their data.

Raw Data 124

Raw Data Relational Database Consulting Architecture 124

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. The journey begins with collecting data from various sources, including internal databases, external repositories, and third-party providers.

Data Science 40

Data Science Building AWS Data Lake 40

ProjectPro

JUNE 6, 2025

Data engineers are responsible for these data integration and ELT tasks, where the initial step requires extracting data from different types of databases/files, such as RDBMS, flat files, etc. Engineers can also use the "LOAD DATA INFILE" command to extract data from flat files like CSV or TXT.

Data Engineering 45

Data Engineering Data Engineer SQL Engineering 45

ProjectPro

JUNE 6, 2025

Think of the data integration process as building a giant library where all your data's scattered notebooks are organized into chapters. You define clear paths for data to flow, from extraction (gathering structured/unstructured data from different systems) to transformation (cleaning the raw data, processing the data, etc.)

Data Integration 40

Data Integration Project Data Lake PostgreSQL 40

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.

Data Pipeline 64

Data Pipeline AWS Project Building 64

ProjectPro

JUNE 6, 2025

If someone is looking to master the art and science of constructing batch pipelines, ProjectPro has got you covered with this comprehensive tutorial that will help you learn how to build your first batch data pipeline and transform raw data into actionable insights. Data Storage- Processed data needs a destination for storage.

Data Pipeline 40

Data Pipeline Building Retail Data Ingestion 40

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 40

Big Data Hadoop Relational Database AWS 40

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. The process begins with the establishment of individual staging models for each data source.

Raw Data 40

Raw Data SQL Datasets Data Pipeline 40

ProjectPro

JUNE 6, 2025

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 40

Data Pipeline Architecture Kafka Data Lake 40

ProjectPro

JUNE 6, 2025

From working with raw data in various formats to the complex processes of transforming and loading data into a central repository and conducting in-depth data analysis using SQL and advanced techniques, you will explore a wide range of real-world databases and tools.

Data Engineer 45

Data Engineer Data Engineering Cloud Engineering 45

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 40

Data Analysis Data Analytics Process Insurance 40

ProjectPro

JUNE 6, 2025

Traditional ETL processes have long been a bottleneck for businesses looking to turn raw data into actionable insights. Amazon, which generates massive volumes of data daily, faced this exact challenge. This method leverages In-Memory Data Grids (IMDG) to store and cache data, providing fast, real-time query responses.

Data Analytics 40

Data Analytics MySQL PostgreSQL Data Lake 40

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 40

Data Science Data Engineer Data Engineering Engineering 40

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 Engineer 59

Data Engineer Data Engineering Project Engineering 59

ProjectPro

JUNE 6, 2025

Therefore, this is another beneficial data migration use case scenario worth exploring. You can migrate SQL Server running on-premises or on SQL Server on Virtual Machines, Amazon EC2, Amazon RDS (Relational Database Service) for SQL Server, or even on Google Compute Engine. This necessitates data consolidation.

Project 40

Project Google Cloud AWS MongoDB 40

ProjectPro

JUNE 6, 2025

AWS DynamoDB An alternative to relational databases, Amazon DynamoDB's NoSQL database supports several different data formats, including document, graph, key-value, memory, and search. This generates highly functional, scalable, adaptable, and efficient databases for modern workloads.

AWS 40

AWS Data Engineering Data Engineer Engineering 40

ProjectPro

JUNE 6, 2025

Vector Databases primarily excel in similarity search, which involves finding objects in the database that closely resemble a given query object based on their vector representations. Vector Databases stand out in their ability to handle large-scale high-dimensional datasets efficiently and perform rapid similarity searches.

Database 40

Database Algorithm Machine Learning Metadata 40

ProjectPro

JUNE 6, 2025

Differentiate between relational and non-relational database management systems. Relational Database Management Systems (RDBMS) Non-relational Database Management Systems Relational Databases primarily work with structured data using SQL (Structured Query Language).

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

ProjectPro

JUNE 6, 2025

Data mining methods are cost-effective and efficient compared to other statistical data applications. Data warehouses, on the other hand, simplify every type of business data. The majority of the user's effort is inputting raw data. A virtual data warehouse offers a collective view of the completed data.

Data Warehouse 40

Data Warehouse Data Mining Recruitment Database 40

U-Next

MARCH 7, 2023

Taking data from sources and storing or processing it is known as data extraction. Define Data Wrangling The process of data wrangling involves cleaning, structuring, and enriching raw data to make it more useful for decision-making. Data is discovered, structured, cleaned, enriched, validated, and analyzed.

Data Mining 96

Data Mining Scala Government Data Governance 96

ProjectPro

JUNE 6, 2025

Therefore, data engineers must gain a solid understanding of these Big Data tools. Machine Learning Machine learning helps speed up the processing of humongous data by identifying trends and patterns. It is possible to classify raw data using machine learning algorithms , identify trends, and turn data into insights.

Data Engineer 59

Data Engineer Data Engineering Engineering ETL Tools 59

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. Users can explore data, uncover trends, and share their findings with stakeholders.

Machine Learning 40

Machine Learning AWS Amazon Web Services Deep Learning 40

ProjectPro

JUNE 6, 2025

Fusion RAG Architecture Fusion RAG extends the retrieval process by combining information from multiple sources—structured (like relational databases or APIs) and unstructured (documents, PDFs, or web pages). Reference Research Paper: [link] 4. Metadata, like document titles or URLs, is extracted to aid in accurate querying.

Architecture 52

Architecture Data Ingestion Google Cloud AWS 52

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 Engineer 40

Data Engineer Data Engineering Engineering ETL Tools 40

Knowledge Hut

DECEMBER 26, 2023

Businesses benefit at large with these data collection and analysis as they allow organizations to make predictions and give insights about products so that they can make informed decisions, backed by inferences from existing data, which, in turn, helps in huge profit returns to such businesses. What is the role of a Data Engineer?

Data Engineer 98

Data Engineer Data Engineering Engineering Pipeline-centric 98

Precisely

MARCH 9, 2023

We will also address some of the key distinctions between platforms like Hadoop and Snowflake, which have emerged as valuable tools in the quest to process and analyze ever larger volumes of structured, semi-structured, and unstructured data. Flexibility Data lakes are, by their very nature, designed with flexibility in mind.

Data Lake 59

Data Lake Data Warehouse Hadoop Raw Data 59

Striim

SEPTEMBER 11, 2024

Third-Party Data: External data sources that your company does not collect directly but integrates to enhance insights or support decision-making. These data sources serve as the starting point for the pipeline, providing the raw data that will be ingested, processed, and analyzed.

Data Pipeline 98

Data Pipeline Designing Data Lake Data Warehouse 98

Cloudera

NOVEMBER 2, 2020

Data processing and analytics drive their entire business. So they needed a data warehouse that could keep up with the scale of modern big data systems , but provide the semantics and query performance of a traditional relational database. Optimized access to both full fidelity raw data and aggregations.

Data Warehouse 97

Data Warehouse Kafka Lambda Architecture Telecommunication 97

ProjectPro

JUNE 6, 2025

Start by grasping key concepts, data types, and structures. Understand basic data cleaning techniques to prepare raw data for analysis. Build a Job Winning Data Engineer Portfolio with Solved End-to-End Big Data Projects 3. SQL provides direct access to this treasure trove of data.

Portfolio 40

Portfolio Programming Language Hadoop Consulting 40

ProjectPro

FEBRUARY 8, 2023

But this data is not that easy to manage since a lot of the data that we produce today is unstructured. In fact, 95% of organizations acknowledge the need to manage unstructured raw data since it is challenging and expensive to manage and analyze, which makes it a major concern for most businesses.

AWS 98

AWS Scala Metadata Data Lake 98

Cloud Academy

JANUARY 27, 2022

A data engineer is an engineer who creates solutions from raw data. A data engineer develops, constructs, tests, and maintains data architectures. Let’s review some of the big picture concepts as well finer details about being a data engineer. Earlier we mentioned ETL or extract, transform, load.

Certification 52

Certification Data Engineer Data Engineering Engineering 52

U-Next

SEPTEMBER 7, 2022

Autonomous data warehouse from Oracle. . The Snowflake database. . What is Data Lake? . Essentially, a data lake is a repository of raw data from disparate sources. A data lake stores current and historical data similar to a data warehouse. These are systems for storing data. .

Data Lake 52

Data Lake Data Warehouse Unstructured Data Amazon Web Services 52