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 98

Data Integration Metadata Government Unstructured Data 98

DataKitchen

DECEMBER 9, 2022

ChatGPT> DataOps, or data operations, is a set of practices and technologies that organizations use to improve the speed, quality, and reliability of their data analytics processes. One of the key benefits of DataOps is the ability to accelerate the development and deployment of data-driven solutions.

Machine Learning 52

Machine Learning Data Preparation Government Data Analytics 52

Cloudera

DECEMBER 16, 2022

We’re proud to be recognized for the data management and data analytics innovations we have delivered in the new Cloudera Data Platform (CDP). Cloudera has always been in the forefront of disruptive technical innovation in data platforms. 6-Operational efficiency to optimize workload performance and cost.

Database 92

Database Cloud Systems Management 92

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

Knowledge Hut

JUNE 16, 2023

In today's data-driven world, organizations are trying to find valuable insights from the vast sets of data available to them. That is where Data analytics comes into the picture - guiding organizations to make smarter decisions by utilizing statistical and computational methods. What is Data Analytics?

Programming Language 52

Programming Language Data Science Data Analytics Cloud Computing 52

Knowledge Hut

FEBRUARY 29, 2024

Currently, numerous resources are being created on the internet consisting of data science websites, data analytics websites, data science portfolio websites, data scientist portfolio websites and so on. So, having the right knowledge of tools and technology is important for handling such data.

Data Science 98

Data Science Datasets Machine Learning Database Design 98

Knowledge Hut

MAY 1, 2024

It is important to make use of this big data by processing it into something useful so that the organizations can use advanced analytics and insights to their advant age (generating better profits, more customer-reach, and so on). All these are different processes in the world of data analytics.

Algorithm 98

Algorithm Data Science Raw Data Computer Science 98

Precisely

DECEMBER 4, 2023

To thrive amid this rapid change, telecom leaders must carefully consider how they collect, aggregate, enrich, and analyze data to drive well-informed decisions. Data analytics are the key to answering these questions with confidence and building a profitable, growing business, but so is location intelligence.

Telecommunication 64

Telecommunication Data Science Data Integration Data Preparation 64

DataKitchen

JULY 27, 2023

Let’s go through the ten Azure data pipeline tools Azure Data Factory : This cloud-based data integration service allows you to create data-driven workflows for orchestrating and automating data movement and transformation. You can use it for big data analytics and machine learning workloads.

Data Pipeline 52

Data Pipeline BI Machine Learning Data Preparation 52

AltexSoft

JUNE 14, 2021

On top of that, the company uses big data analytics to quantify losses and predict risks by placing the client into a risk group and quoting a relevant premium. The groups are created using algorithms that collect extensive customer data, such as health conditions. You’ll need a data engineering team for that.

Insurance 88

Insurance Medical Machine Learning Data Collection 88

KDnuggets

NOVEMBER 24, 2021

Maintaining a centralized data repository can simplify your business intelligence initiatives. Here are four data integration tools that can make data more valuable for modern enterprises.

Data Integration 160

Data Integration Business Intelligence Data Data Preparation 160

DataKitchen

SEPTEMBER 20, 2021

An enterprise cannot derive value from its data unless data scientists can stay focused on innovation. Errors undermine trust in data and the data team. Less trust means less data-driven decision-making. Errors in data analytics tend to occur in a very public manner. Take a broader view.

Data Pipeline 98

Data Pipeline Process Data Cleanse Architecture 98

Towards Data Science

OCTOBER 12, 2023

Construction engineer investigating his work — Stable diffusion Introduction In our previous publication, From Data Engineering to Prompt Engineering , we demonstrated how to utilize ChatGPT to solve data preparation tasks.

Data Integration 97

Data Integration Systems Engineering Datasets 97

Ascend.io

AUGUST 16, 2023

The analysis found that the platform delivers multiple economic benefits, including major improvements to the productivity of data analytics teams, reduced overall cloud infrastructure costs, lower data platform tooling costs, and greater pipeline reliability.

Data Engineer 52

Data Engineer Data Engineering Engineering Data Pipeline 52

Knowledge Hut

SEPTEMBER 26, 2023

Adding slicers and filters to allow users to control data views. Data Preparation and Transformation Skills Preparing the raw data into the right structure and format is the primary and most important step in data analysis. Creating bookmarks to save and recall specific dashboard views.

BI 52

BI Business Intelligence Raw Data Data Analysis 52

Knowledge Hut

NOVEMBER 3, 2023

Companies around the world, especially fast-paced technology companies, rely on data analytics to turn both structured and unstructured into meaningful insights in the modern world. SAP Analytics Cloud and Microsoft Power BI are two major rivals in this space. Emphasizes collaboration through integration with Microsoft Teams.

BI 52

BI Cloud Retail Medical 52

Knowledge Hut

JUNE 4, 2024

The agency would also use data to track the marketing campaign results and adjust as necessary. Start a Data Analytics Blog If you are thinking about startup ideas for data science, starting a data analytics blog could be a great business idea if you are passionate about data analytics and enjoy sharing your insights with others.

Data Science 52

Data Science Data Mining Media Recruitment 52

ProjectPro

FEBRUARY 8, 2023

Another leading European company, Claranet, has adopted Glue to migrate their data load from their existing on-premise solution to the cloud. The popular data integration tool, AWS Glue, enables data analytics users to quickly acquire, analyze, migrate, and integrate data from multiple sources.

AWS 98

AWS Scala Metadata Data Lake 98

Rockset

MARCH 24, 2022

Even though the complexity, data shape and data volume are increasing and changing, companies are looking for simpler and faster database solutions. More so now than before, companies want to easily query data across different sources without worrying about data ops.

Non-relational Database 52

Non-relational Database Relational Database Database Data Pipeline 52

DataKitchen

FEBRUARY 21, 2023

Make Trusted Data Products with Reusable Modules : “Many organizations are operating monolithic data systems and processes that massively slow their data delivery time.” Marcus has inherited a team in which individual ‘heroes’ built data analytics as a set of side projects without consistency or management.

Data Engineer 52

Data Engineer Data Engineering Engineering Pipeline-centric 52

Knowledge Hut

OCTOBER 11, 2023

Microsoft Power BI is a business intelligence and data analytics software that is used by data professionals including data scientists, Power BI developers, data analysts, etc. As a beginner, you will learn the core concepts of how to turn data into cool reports and charts. What is Microsoft Power BI?

BI 52

BI Business Intelligence Data Analysis Data Preparation 52

U-Next

SEPTEMBER 7, 2022

People who are unfamiliar with unprocessed data often find it difficult to navigate data lakes. Usually, raw, unstructured data needs to be analyzed and translated by a data scientist using specialized tools. . However, data lakes aren’t only limited to data lake storage.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Amazon Web Services 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. to schedule the project activities.

Big Data 52

Big Data Data Engineer Data Engineering Engineering 52

Advancing Analytics: Data Engineering

JUNE 21, 2019

Hear me out – back in the on-premises days we had data loading processes that connect directly to our source system databases and perform huge data extract queries as the start of one long, monolithic data pipeline, resulting in our data warehouse. Are you still bound by source-system access?

Data Warehouse 52

Data Warehouse Business Intelligence Data Data Validation 52

ProjectPro

JANUARY 31, 2023

In this blog, we'll dive into some of the most commonly asked big data interview questions and provide concise and informative answers to help you ace your next big data job interview. Get ready to expand your knowledge and take your big data career to the next level! “Data analytics is the future, and the future is NOW!

Big Data 52

Big Data Hadoop Relational Database AWS 52

ProjectPro

JUNE 2, 2015

2015 will be the year that many big data companies will take their big data analytics to the next level by turning big data into actionable insights. This will supercharge the marketing tactics of the business and make data precious than ever. said Dirk Ruger, Head of After-Sale Analytics at BMW.

Big Data 40

Big Data Hadoop Banking Data Analytics 40

Knowledge Hut

SEPTEMBER 26, 2023

Organisations are constantly looking for robust and effective platforms to manage and derive value from their data in the constantly changing landscape of data analytics and processing. These platforms provide strong capabilities for data processing, storage, and analytics, enabling companies to fully use their data assets.

Data Lake 52

Data Lake Database-centric Pipeline-centric Machine Learning 52

ProjectPro

JANUARY 24, 2023

Time-saving: SageMaker automates many of the tasks, by creating a pipeline starting from data preparation and ML model training, which saves time and resources. Data Flow – A data flow allows you to specify a series of steps for preparing data for machine learning.

Building 52

Building Algorithm Machine Learning AWS 52

Knowledge Hut

JUNE 20, 2023

The generalist position would suit a data scientist looking for a transition into a data engineer. Pipeline-Centric Engineer: These data engineers prefer to serve in distributed systems and more challenging projects of data science with a midsize data analytics team.

Machine Learning 52

Machine Learning Data Engineer Data Engineering Engineering 52

AltexSoft

MARCH 30, 2023

Watch our video to learn more about one of the key Databricks applications — data engineering. How data engineering works in 14 minutes. It’s worth noting that Databricks facilitates DevOps practices and adds automation to the data analytics lifecycle ( DataOps ). And here are several more reasons in favor of this choice.

Scala 64

Scala Data Lake Machine Learning BI 64

ProjectPro

MAY 20, 2015

It is difficult to stay up-to-date with the latest developments in IT industry especially in a fast growing area like big data where new big data companies, products and services pop up daily. With the explosion of Big Data, Big data analytics companies are rising above the rest to dominate the market.

Big Data 40

Big Data Hadoop Business Analyst Data Analytics 40

U-Next

JUNE 29, 2022

Preparing data for analysis is known as extract, transform and load (ETL). While the ETL workflow is becoming obsolete, it still serves as a common word for the data preparation layers in a big data ecosystem. Working with large amounts of data necessitates more preparation than working with less data.

Big Data 40

Big Data Big Data Ecosystem Data Lake Raw Data 40

Edureka

AUGUST 27, 2024

A McKinsey report shows that nearly all employees will leverage data to augment their work by 2025. However, organisations don’t effectively ingest and process all useful information mainly because of the lack of data analytic infrastructure.

BI 52

BI Business Intelligence AWS Amazon Web Services 52

ProjectPro

AUGUST 24, 2021

So, working on a data warehousing project that helps you understand the building blocks of a data warehouse is likely to bring you more clarity and enhance your productivity as a data engineer. Data Analytics: A data engineer works with different teams who will leverage that data for business solutions.

Data Engineer 52

Data Engineer Data Engineering Coding Project 52

Edureka

JULY 4, 2024

Azure Synapse is Microsoft’s cloud-based analytics powerhouse. It’s a Swiss Army knife for data pros, merging data integration, warehousing, and big data analytics into one sleek package. What is Azure Synapse? At its core, Azure Synapse combines the power of SQL and Apache Spark technologies.

Data Lake 40

Data Lake Pipeline-centric Data Warehouse ETL Tools 40

ProjectPro

OCTOBER 6, 2021

GCP offers 90 services that span computation, storage, databases, networking, operations, development, data analytics , machine learning , and artificial intelligence , to name a few. Get FREE Access to Data Analytics Example Codes for Data Cleaning, Data Munging, and Data Visualization 2.

Google Cloud 52

Google Cloud Project Data Lake Healthcare 52

AltexSoft

DECEMBER 15, 2021

Namely, AutoML takes care of routine operations within data preparation, feature extraction, model optimization during the training process, and model selection. In the meantime, we’ll focus on AutoML which drives a considerable part of the MLOps cycle, from data preparation to model validation and getting it ready for deployment.

Machine Learning 59

Machine Learning Deep Learning Algorithm Telecommunication 59