ThoughtSpot

JUNE 2, 2025

For data analysts and engineers, the journey from raw data to actionable business insights for business users is never as simple as it sounds. The semantic layer is a critical component in this process, serving as the bridge between complex data sources and the business logic required for informed decision-making.

Raw Data 64

Raw Data Government Algorithm Data 64

Cloudera

DECEMBER 4, 2024

For example: An AWS customer using Cloudera for hybrid workloads can now extend analytics workflows to Snowflake, gaining deeper insights without moving data across infrastructures. Or now customers can combine Cloudera’s raw data processing and Snowflake’s analytical capabilities to build efficient AI/ML pipelines.

AWS 89

AWS Raw Data Relational Database Government 89

Monte Carlo

NOVEMBER 12, 2024

A data engineering architecture is the structural framework that determines how data flows through an organization – from collection and storage to processing and analysis. It’s the big blueprint we data engineers follow in order to transform raw data into valuable insights.

Architecture 96

Architecture Data Engineer Data Engineering Engineering 96

Snowflake

NOVEMBER 11, 2024

However, scaling LLM data processing to millions of records can pose data transfer and orchestration challenges, easily addressed by the user-friendly SQL functions in Snowflake Cortex. Additionally, we launched cross-region inference , allowing you to access preferred LLMs even if they aren’t available in your primary region.

Unstructured Data 98

Unstructured Data SQL AWS Healthcare 98

The Pragmatic Engineer

OCTOBER 17, 2024

The startup was able to start operations thanks to getting access to an EU grant called NGI Search grant. As always, I have not been paid to write about this company and have no affiliation with it – see more in my ethics statement. Funding and team size The company got started thanks to a €150K ($165K) EU grant.

Cloud 336

Cloud Metadata AWS Cloud Computing 336

The Pragmatic Engineer

JANUARY 12, 2023

In this week’s The Scoop, I analyzed this information and dissected it, going well beyond the raw data. Here are a few details from the data points, focusing on software engineering compensation. How can you use this data in budgeting, and what are the caveats to be aware of?

Software Engineer 230

Software Engineer Software Engineering Engineering Raw Data 230

ProjectPro

JUNE 6, 2025

Using familiar SQL as Athena queries on raw data stored in S3 is easy; that is an important point, and you will explore real-world examples related to this in the latter part of the blog. It is compatible with Amazon S3 when it comes to data storage data as there is no requirement for any other storage mechanism to run the queries.

AWS 67

AWS Big Data SQL Raw Data 67

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. Tools such as SQL engines, BI tools (e.g.,

Data Lake 40

Data Lake Building Hadoop Raw Data 40

ProjectPro

JUNE 6, 2025

This blog covers the top ten AWS data engineering tools popular among data engineers across the big data industry. Additionally, engineers can build schemas and tables, import data visually, and explore database objects using Query Editor v2. Get Started with Learning Python for Data Engineering Now !

AWS 52

AWS Data Engineering Data Engineer Engineering 52

ProjectPro

JUNE 6, 2025

By using the Parquet-based open-format storage layer, Delta Lake is able to solve the shortcomings of data lakes and unlock the full potential of a company's data. This helps data scientists and business analysts access and analyze all the data at their disposal. How to access Delta lake on Azure Databricks?

Data Lake 63

Data Lake Data Warehouse Metadata Unstructured Data 63

ProjectPro

JUNE 6, 2025

We will now describe the difference between these three different career titles, so you get a better understanding of them: Data Engineer A data engineer is a person who builds architecture for data storage. They can store large amounts of data in data processing systems and convert raw data into a usable format.

Engineering 75

Engineering Deep Learning Software Engineer Software Engineering 75

Teradata

MAY 30, 2025

Infrastructure layout Diagram illustrating the data flow between each component of the infrastructure Prerequisites Before you embark on this integration, ensure you have the following set up: Access to a Vantage instance: If you need a test instance of Vantage, you can provision one for free Python 3.10 dbt-core dagster==1.7.9

Data Integration 52

Data Integration Raw Data Metadata Data Pipeline 52

Snowflake

JANUARY 29, 2024

Google Analytics, a tool widely used by marketers, provides invaluable insights into website performance, user behavior and critical analytic data that helps marketers understand the customer journey and improve marketing ROI. In the case of raw data, it replicates it directly from the BigQuery storage layer.

Raw Data 126

Raw Data Aggregated Data Government Cloud 126

ProjectPro

JUNE 6, 2025

Today, data engineers are constantly dealing with a flood of information and the challenge of turning it into something useful. The journey from raw data to meaningful insights is no walk in the park. It requires a skillful blend of data engineering expertise and the strategic use of tools designed to streamline this process.

Data Pipeline 49

Data Pipeline Google Cloud AWS Kafka 49

Edureka

MAY 27, 2025

OneLake Data Lake OneLake provides a centralized data repository and is the fundamental storage layer of Microsoft Fabric. It preserves security and governance while facilitating smooth data access across all Fabric services. Transform Your Data Analytics with Microsoft Fabric!

Architecture 52

Architecture BI Business Intelligence Data Lake 52

ThoughtSpot

JANUARY 15, 2025

Thats why were excited to announce the launch of Analyst Studio , the collaborative creator space where data teams can come together to transform raw data into actionable insights. Now you can join data from multiple sources and directly manipulate raw datamaximizing flexibility and enabling quick iterations.

BI 64

BI SQL Data Warehouse Raw Data 64

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

Ascend.io

OCTOBER 28, 2024

What is Data Transformation? Data transformation is the process of converting raw data into a usable format to generate insights. It involves cleaning, normalizing, validating, and enriching data, ensuring that it is consistent and ready for analysis.

Raw Data 52

Raw Data Aggregated Data Data Pipeline Data Validation 52

ProjectPro

JUNE 6, 2025

Significance of Data Preparation Process in Machine Learning Data Preparation Steps for Machine Learning Projects Machine Learning Data Preparation Tools Project Ideas for Data Preparation in Machine Learning FAQs on Preparing Data for Machine Learning What is Data Preparation for Machine Learning?

Data Preparation 45

Data Preparation Machine Learning Project IT 45

ProjectPro

JUNE 6, 2025

Independent, Isolated Data Processing Resources: Independence and isolation of data processing resources within the pipeline ensure resilience and reliability, minimizing the risk of failures or disruptions and preserving data integrity and operational stability.

Data Science 40

Data Science Building AWS Data Lake 40

ProjectPro

JUNE 6, 2025

Security AWS offers various security features companies can use to protect their data, such as encryption, access controls, and network isolation. This ensures that companies' data is always protected and secure. AWS provides various relational and non-relational data stores that act as data sources in an ETL pipeline.

AWS 40

AWS Data Management ETL Tools Management 40

ProjectPro

JUNE 6, 2025

Load- The pipeline copies data from the source into the destination system, which could be a data warehouse or a data lake. Transform- Organizations routinely transform raw data in various ways and use it with multiple tools or business processes. Scalability ELT can be highly adaptable when using raw data.

Data Engineering 40

Data Engineering Data Engineer Engineering Data Lake 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. Organizations use cloud data warehouses like AWS Redshift to organize such information at scale.

Data Pipeline 64

Data Pipeline AWS Project Building 64

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. Accessing this information lets you engage in profitable stocks and ventures and make better financial decisions.

BI 45

BI ETL Tools Retail Healthcare 45

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.

Raw Data 40

Raw Data SQL Datasets Data Pipeline 40

KDnuggets

JUNE 12, 2025

View full parsed raw data") print("2. The PDF I’m using is publicly accessible, and you can download it using the link. View full parsed raw data 2. print("What would you like to do?") Extract full plain text") print("3. Get LangChain documents (no chunking)") print("4. Show document metadata") print("6.

Building 57

Building Metadata Raw Data Data Science 57

ProjectPro

JUNE 6, 2025

The Data Cleaning Pipeline Let's assume we have clients sending hotel booking demand data from multiple data sources to a scalable storage solution. Before analyzing the raw data, we need to clean it and then load it into a database where it can be accessed for analysis. Our Airflow DAG will have two tasks.

Data Pipeline 45

Data Pipeline Building Data Lake Raw Data 45

ProjectPro

JUNE 6, 2025

Emily is an experienced big data professional in a multinational corporation. As she deals with vast amounts of data from multiple sources, Emily seeks a solution to transform this raw data into valuable insights. dbt and Snowflake: Building the Future of Data Engineering Together."

Pipeline-centric 40

Pipeline-centric Database-centric Raw Data Data Warehouse 40

ProjectPro

JUNE 6, 2025

Key Features: Along with direct connections to Google Cloud's streaming services like Dataflow, BigQuery includes built-in streaming capabilities that instantly ingest streaming data and make it readily accessible for querying. Get Started with Learning Python for Data Engineering Now !

Data Engineering 40

Data Engineering Data Engineer Engineering Google Cloud 40

ProjectPro

JUNE 6, 2025

Business Intelligence and Artificial Intelligence are popular technologies that help organizations turn raw data into actionable insights. While both BI and AI provide data-driven insights, they differ in how they help businesses gain a competitive edge in the data-driven marketplace. PREVIOUS NEXT <

Business Intelligence 56

Business Intelligence BI Data Mining Raw Data 56

Towards Data Science

FEBRUARY 6, 2024

As you do not want to start your development with uncertainty, you decide to go for the operational raw data directly. Accessing Operational Data I used to connect to views in transactional databases or APIs offered by operational systems to request the raw data. Does it sound familiar?

Systems 98

Systems Raw Data Data Cleanse Metadata 98

ProjectPro

JUNE 6, 2025

They often deal with big data (structured, unstructured, and semi-structured) to generate reports to identify patterns, gain valuable insights, and produce visualizations easily deciphered by stakeholders and non-technical business users. Creating dashboards and tools for business users based on analysis by data analysts and data scientists.

Data Science 56

Data Science Data Mining Data Architect BI 56

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 45

Data Engineering Data Engineer SQL Engineering 45

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. What is a Big Data Pipeline?

Data Pipeline 40

Data Pipeline Architecture Kafka Data Lake 40

Snowflake

APRIL 16, 2025

In this edition, discover how Houssam Fahs, CEO and Co-founder of KAWA Analytics , is on a mission to revolutionize the creation of data-driven applications with a cutting-edge, AI-native platform built for scalability. The drive to democratize powerful tools and redefine how enterprises engage with data motivates me every day.

Building 56

Building Raw Data Data Security Data Analysis 56

ProjectPro

JUNE 6, 2025

Feature Store : Feature stores are used to store variations on the feature set leveraged for machine learning models t hat multiple teams can access. Focus on performing a preliminary analysis of the data using Python, leveraging pandas profiling and sweetviz. The source code for inspiration can be found here.

Project 66

Project Amazon Web Services Machine Learning Data Science 66

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 Raw Data 40