The Modern Data Company

MAY 10, 2023

To choose the most suitable data management solution for your organization, consider the following factors: Data types and formats: Do you primarily work with structured, unstructured, or semi-structured data? Consider whether you need a solution that supports one or multiple data formats.

Data Management 52

Data Management Data Lake Management Data Warehouse 52

U-Next

MARCH 1, 2023

Being a hybrid role, Data Engineer requires technical as well as business skills. They build scalable data processing pipelines and provide analytical insights to business users. A Data Engineer also designs, builds, integrates, and manages large-scale data processing systems. What is AWS Kinesis?

Data Engineering 75

Data Engineering Data Engineer Non-relational Database Engineering 75

The Modern Data Company

MAY 10, 2023

To choose the most suitable data management solution for your organization, consider the following factors: Data types and formats: Do you primarily work with structured, unstructured, or semi-structured data? Consider whether you need a solution that supports one or multiple data formats.

Data Management 52

Data Management Management Data Lake Data Warehouse 52

The Modern Data Company

MAY 10, 2023

To choose the most suitable data management solution for your organization, consider the following factors: Data types and formats: Do you primarily work with structured, unstructured, or semi-structured data? Consider whether you need a solution that supports one or multiple data formats.

Data Management 52

Data Management Management Data Lake Data Warehouse 52

Data Engineering Weekly

JUNE 4, 2023

link] Uber: Spark Analysers: Catching Anti-Patterns In Spark Apps One of the challenges in commoditizing data processing engines like Spark is that it requires an expert user to understand and operate this system. Many of the real-world data, all the way from medical images to astro monitoring, are unstructured data.

Data Engineering 52

Data Engineering Data Engineer Engineering Medical 52

Cloudera

SEPTEMBER 17, 2020

They were not able to quickly and easily query and analyze huge amounts of data as required. They also needed to combine text or other unstructured data with structured data and visualize the results in the same dashboards. Text data served up via Solr’s powerful analytics engine and APIs.

Unstructured Data 80

Unstructured Data Data Warehouse Pharmaceutical MySQL 80

AltexSoft

AUGUST 29, 2023

Data sources can be broadly classified into three categories. Structured data sources. These are the most organized forms of data, often originating from relational databases and tables where the structure is clearly defined. Semi-structured data sources. Unstructured data sources.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Ascend.io

NOVEMBER 21, 2023

The emergence of cloud data warehouses, offering scalable and cost-effective data storage and processing capabilities, initiated a pivotal shift in data management methodologies. Extract The initial stage of the ELT process is the extraction of data from various source systems.

Raw Data 52

Raw Data Data Warehouse Data Cleanse Data Integration 52

Grouparoo

JANUARY 11, 2022

Typical applications are in scientific experimentation and observation processes where data consumers will not fully understand the nature of the data until after the completion of data processing and analysis. A data lake offers the ideal solution for storing such data of unknown relationships.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

Silectis

JANUARY 3, 2021

BI (Business Intelligence) Strategies and systems used by enterprises to conduct data analysis and make pertinent business decisions. Big Data Large volumes of structured or unstructured data. Big Query Google’s cloud data warehouse. Data Visualization Graphic representation of a set or sets of data.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

Monte Carlo

APRIL 24, 2023

A notebook-based environment allows data engineers, data scientists, and analysts to work together seamlessly, streamlining data processing, model development, and deployment. Databricks also pioneered the modern data lakehouse architecture, which combines the best of data lakes and data warehouses.

Data Lake 52

Data Lake Google Cloud Data Warehouse AWS 52

Knowledge Hut

DECEMBER 26, 2023

The responsibilities of Data Analysts are to acquire massive amounts of data, visualize, transform, manage and process the data, and prepare data for business communications. The primary responsibility of a Data Scientist is to provide actionable business insights based on their analysis of the data.

Data Science 97

Data Science BI Business Intelligence Data Mining 97

ProjectPro

SEPTEMBER 9, 2021

Apache Hive and Apache Spark are the two popular Big Data tools available for complex data processing. To effectively utilize the Big Data tools, it is essential to understand the features and capabilities of the tools. Spark SQL, for instance, enables structured data processing with SQL.

Hadoop 52

Hadoop Big Data Tools Java SQL 52

Knowledge Hut

NOVEMBER 28, 2023

The data goes through various stages, such as cleansing, processing, warehousing, and some other processes, before the data scientists start analyzing the data they have garnered. The data analysis stage is important as the data scientists extract value and knowledge from the processed, structured data.

Data Science 52

Data Science Telecommunication Recruitment Finance 52

ProjectPro

DECEMBER 7, 2021

It can also consist of simple or advanced processes like ETL (Extract, Transform and Load) or handle training datasets in machine learning applications. In broader terms, two types of data -- structured and unstructured data -- flow through a data pipeline.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

Knowledge Hut

JULY 26, 2023

What is Big Data? Big Data is the term used to describe extraordinarily massive and complicated datasets that are difficult to manage, handle, or analyze using conventional data processing methods. Variety: Variety represents the diverse range of data types and formats encountered in Big Data.

Big Data 52

Big Data Data Cleanse Retail Healthcare 52

Rockset

JULY 29, 2022

This fast, serverless, highly scalable, and cost-effective multi-cloud data warehouse has built-in machine learning, business intelligence, and geospatial analysis capabilities for querying massive amounts of structured and semi-structured data. So, it’s not real-time data. Pricing starts at $0.25

Data Analytics 52

Data Analytics Data Warehouse Datasets Google Cloud 52

Knowledge Hut

JUNE 16, 2023

Data preparation: Because of flaws, redundancy, missing numbers, and other issues, data gathered from numerous sources is always in a raw format. After the data has been extracted, data analysts must transform the unstructured data into structured data by fixing data errors, removing unnecessary data, and identifying potential data.

Programming Language 52

Programming Language Cloud Computing Data Preparation Data Science 52

Edureka

AUGUST 2, 2023

Raw data, however, is frequently disorganised, unstructured, and challenging to work with directly. Data processing analysts can be useful in this situation. Let’s take a deep dive into the subject and look at what we’re about to study in this blog: Table of Contents What Is Data Processing Analysis?

Data Process 52

Data Process Process Data Cleanse Data Mining 52

Knowledge Hut

MAY 31, 2023

Companies utilize different approaches to deal with data in order to extract information from structured, semi-structured, or unstructured data sets. Business Intelligence is one such approach that helps professionals to extract valuable information from structured data.

Business Intelligence 52

Business Intelligence BI Business Analyst Consulting 52

Ascend.io

AUGUST 31, 2023

For example, unlike traditional platforms with set schemas, data lakes adapt to frequently changing data structures at points where the data is loaded , accessed, and used. As a result, transformation processing inside data warehouses is becoming more accessible, and maintaining a separate data lake is no longer needed.

Data Lake 52

Data Lake ETL Tools Data Warehouse Data Pipeline 52

ProjectPro

OCTOBER 15, 2014

Generally data to be stored in the database is categorized into 3 types namely Structured Data, Semi Structured Data and Unstructured Data. We generally refer to Unstructured Data as “Big Data” and the framework that is used for processing Big Data is popularly known as Hadoop.

Hadoop 52

Hadoop Unstructured Data Java SQL 52

ProjectPro

SEPTEMBER 30, 2014

While the initial era of ETL ignited enough sparks and got everyone to sit up, take notice and applaud its capabilities, its usability in the era of Big Data is increasingly coming under the scanner as the CIOs start taking note of its limitations.

Hadoop 52

Hadoop Unstructured Data ETL Tools ETL System 52

ProjectPro

JULY 22, 2016

Data lake is gaining momentum across various organizations and everyone wants to know how to implement a data lake and why. There are several people writing that data lakes are replacing data warehouses but this is just another technology hype that is coming across the effective use of data.

Data Lake 40

Data Lake Data Warehouse Hadoop Unstructured Data 40

Knowledge Hut

JANUARY 19, 2024

With businesses relying heavily on data, the demand for skilled data scientists has skyrocketed. In data science, we use various tools, processes, and algorithms to extract insights from structured and unstructured data. Facilitating data-driven decision-making through visuals.

Data Science 52

Data Science Machine Learning Datasets Algorithm 52

Knowledge Hut

DECEMBER 29, 2023

A Data Engineer's primary responsibility is the construction and upkeep of a data warehouse. In this role, they would help the Analytics team become ready to leverage both structured and unstructured data in their model creation processes. It separates the hidden links and patterns in the data.

Data Science 98

Data Science Data Mining Deep Learning Programming Language 98

ProjectPro

NOVEMBER 14, 2015

Hadoop projects make optimum use of ever-increasing parallel processing capabilities of processors and expanding storage spaces to deliver cost-effective, reliable solutions. Owned by Apache Software Foundation, Apache Spark is an open-source data processing framework. Why Apache Spark?

Hadoop 52

Hadoop Project Big Data Healthcare 52

Monte Carlo

AUGUST 25, 2023

Understanding data warehouses A data warehouse is a consolidated storage unit and processing hub for your data. Teams using a data warehouse usually leverage SQL queries for analytics use cases. This same structure aids in maintaining data quality and simplifies how users interact with and understand the data.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

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

MAY 20, 2015

Several big data companies are looking to tame the zettabyte’s of BIG big data with analytics solutions that will help their customers turn it all in meaningful insights. The products and services of Cloudera are changing the economics of big data analysis , BI, data processing and warehousing through Hadooponomics.

Big Data 40

Big Data Hadoop Business Analyst Unstructured Data 40

The Modern Data Company

MAY 8, 2023

It can store any type of data — structured, unstructured, and semi-structured — in its native format, providing a highly scalable and adaptable solution for diverse data needs. Data is stored in a schema-on-write approach, which means data is cleaned, transformed, and structured before storing.

Data Management 52

Data Management Data Lake Management Data Governance 52

The Modern Data Company

MAY 8, 2023

It can store any type of data — structured, unstructured, and semi-structured — in its native format, providing a highly scalable and adaptable solution for diverse data needs. Data is stored in a schema-on-write approach, which means data is cleaned, transformed, and structured before storing.

Data Management 52

Data Management Data Lake Management Data Governance 52

The Modern Data Company

MAY 8, 2023

It can store any type of data — structured, unstructured, and semi-structured — in its native format, providing a highly scalable and adaptable solution for diverse data needs. Data is stored in a schema-on-write approach, which means data is cleaned, transformed, and structured before storing.

Data Management 52

Data Management Data Lake Management Data Governance 52

Edureka

JANUARY 18, 2024

For those looking to start learning in 2024, here is a data science roadmap to follow. What is Data Science? Data science is the study of data to extract knowledge and insights from structured and unstructured data using scientific methods, processes, and algorithms.

Data Science 40

Data Science Deep Learning NoSQL Machine Learning 40

Knowledge Hut

MARCH 27, 2024

Big data tools are used to perform predictive modeling, statistical algorithms and even what-if analyses. Some important big data processing platforms are: Microsoft Azure. Why Is Big Data Analytics Important? Let's check some of the best big data analytics tools and free big data analytics tools.

Big Data 52

Big Data Data Analytics MongoDB Big Data Tools 52

Knowledge Hut

JULY 24, 2023

NoSQL Databases NoSQL databases are non-relational databases (that do not store data in rows or columns) more effective than conventional relational databases (databases that store information in a tabular format) in handling unstructured and semi-structured data.

Database 52

Database NoSQL Management Relational Database 52

Knowledge Hut

DECEMBER 26, 2023

They are also accountable for communicating data trends. Let us now look at the three major roles of data engineers. Generalists They are typically responsible for every step of the data processing, starting from managing and making analysis and are usually part of small data-focused teams or small companies.

Data Engineering 98

Data Engineering Data Engineer Engineering Pipeline-centric 98