The Modern Data Company

MAY 10, 2023

Data governance and security: Evaluate the native security, data governance, and data quality management features. Because data lakes can have performance limitations for these use cases, a data warehouse may be a better fit. A more flexible solution like a data lake or lakehouse may be better.

Data Management 52

Data Management Management Data Lake Data Warehouse 52

Databand.ai

JULY 19, 2023

ELT offers a solution to this challenge by allowing companies to extract data from various sources, load it into a central location, and then transform it for analysis. The ELT process relies heavily on the power and scalability of modern data storage systems. The data is loaded as-is, without any transformation.

Data Cleanse 52

Data Cleanse Raw Data Data Storage Data Warehouse 52

Monte Carlo

MAY 14, 2024

Depending on the quantity of data flowing through an organization’s pipeline — or the format the data typically takes — the right modern table format can help to make workflows more efficient, increase access, extend functionality, and even offer new opportunities to activate your unstructured data.

Data Lake 52

Data Lake Metadata Hadoop Data Governance 52

Snowflake

NOVEMBER 29, 2023

While the former can be solved by tokenization strategies provided by external vendors, the latter mandates the need for patient-level data enrichment to be performed with sufficient guardrails to protect patient privacy, with an emphasis on auditability and lineage tracking. A conceptual architecture illustrating this is shown in Figure 3.

Metadata 84

Metadata Healthcare Medical Data Storage 84

Snowflake

JANUARY 22, 2024

As part of that transition, Fidelity has consolidated its analytics data into its Enterprise Analytics Platform, which is engineered using the Snowflake Data Cloud, making it easier for teams and departments across the company to access the data they need. Historically, the platform was housed in physical servers.

Engineering 63

Engineering Data Lake Unstructured Data Metadata 63

Knowledge Hut

MARCH 28, 2024

Job Role 1: Azure Data Engineer Azure Data Engineers develop, deploy, and manage data solutions with Microsoft Azure data services. They use many data storage, computation, and analytics technologies to develop scalable and robust data pipelines. GDPR, HIPAA), and industry standards.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Warehouse 52

DareData

JANUARY 30, 2023

In this post, we'll discuss some key data engineering concepts that data scientists should be familiar with, in order to be more effective in their roles. These concepts include concepts like data pipelines, data storage and retrieval, data orchestrators or infrastructure-as-code.

Data Engineering 52

Data Engineering Data Engineer NoSQL Engineering 52

Knowledge Hut

DECEMBER 22, 2023

Statistics are used by data scientists to collect, assess, analyze, and derive conclusions from data, as well as to apply quantifiable mathematical models to relevant variables. Microsoft Excel An effective Excel spreadsheet will arrange unstructured data into a legible format, making it simpler to glean insights that can be used.

Hadoop 98

Hadoop Deep Learning Data Science Machine Learning 98

Monte Carlo

AUGUST 25, 2023

That’s why it’s essential for teams to choose the right architecture for the storage layer of their data stack. But, the options for data storage are evolving quickly. So let’s get to the bottom of the big question: what kind of data storage layer will provide the strongest foundation for your data platform?

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

Monte Carlo

AUGUST 19, 2023

We’ll cover: What is a data platform? To make things a little easier, I’ve outlined the six must-have layers you need to include in your data platform and the order in which many of the best teams choose to implement them. The five must-have layers of a modern data platform Second to “how do I build my data platform?”,

Building 52

Building BI Data Lake Data Governance 52

Knowledge Hut

APRIL 23, 2024

Big Data certification course will support you in learning big data skills from the greatest mentors to help you build a career in big data. Top 10 Disadvantages of Big Data 1. Need for Skilled Personnel We see data in different forms; it can be categorized into structured, semi-structured, and unstructured data.

Big Data 52

Big Data Media Government Big Data Skills 52

phData: Data Engineering

SEPTEMBER 19, 2023

With the amount of data companies are using growing to unprecedented levels, organizations are grappling with the challenge of efficiently managing and deriving insights from these vast volumes of structured and unstructured data. What is a Data Lake? Consistency of data throughout the data lake.

Data Lake 52

Data Lake Process Metadata Data Warehouse 52

Edureka

FEBRUARY 9, 2023

Azure Data Engineering is a rapidly growing field that involves designing, building, and maintaining data processing systems using Microsoft Azure technologies. As a certified Azure Data Engineer, you have the skills and expertise to design, implement and manage complex data storage and processing solutions on the Azure cloud platform.

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 52

AltexSoft

AUGUST 29, 2023

In 2010, a transformative concept took root in the realm of data storage and analytics — a data lake. The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data. Unstructured data sources.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Knowledge Hut

JULY 24, 2023

In-memory Databases For applications that demand real-time data processing, in-memory databases are created. These databases use RAM-based data storage, which offers quicker access and response times than disk-based storage. These databases give users more freedom in how to organize and use data.

Database 52

Database NoSQL Management Relational Database 52

Monte Carlo

APRIL 24, 2023

Data lakes are useful, flexible data storage repositories that enable many types of data to be stored in its rawest state. Notice how Snowflake dutifully avoids (what may be a false) dichotomy by simply calling themselves a “data cloud.” Not to mention seamless integration with the Oracle ecosystem.

Data Lake 52

Data Lake Google Cloud Data Warehouse AWS 52

Knowledge Hut

JUNE 20, 2023

Search-Based Discovery Tools Search-based discovery tools allow users to utilize search terms in order to create and improve views and perform analysis of both structured and unstructured data. Organizations can store and analyze data on remote servers using cloud-based analytics.

Business Analyst 52

Business Analyst Banking Unstructured Data Cloud Computing 52

Knowledge Hut

NOVEMBER 7, 2023

Every day, enormous amounts of data are collected from business endpoints, cloud apps, and the people who engage with them. Cloud computing enables enterprises to access massive amounts of organized and unstructured data in order to extract commercial value. Data storage, management, and access skills are also required.

Cloud Computing 98

Cloud Computing Cloud Amazon Web Services Entertainment 98

Knowledge Hut

JULY 26, 2023

Traditional data sources typically involve structured data, such as databases and spreadsheets. However, Big Data encompasses unstructured data, including text documents, images, videos, social media feeds, and sensor data. Handling this variety of data requires flexible data storage and processing methods.

Big Data 52

Big Data Data Cleanse Retail Healthcare 52

Knowledge Hut

JUNE 23, 2023

Data warehousing to aggregate unstructured data collected from multiple sources. Data architecture to tackle datasets and the relationship between processes and applications. Step 3 - How to Choose Project Management Courses for Data Engineer Learning Path? What’s the Demand for Data Engineers?

Data Engineering 52

Data Engineering Data Engineer Engineering Non-relational Database 52

Edureka

FEBRUARY 7, 2023

They should also be comfortable working with a variety of data sources and types and be able to design and implement data pipelines that can handle structured, semi-structured, and unstructured data.

Data Engineering 52

Data Engineering Data Engineer Engineering Programming Language 52

Monte Carlo

JUNE 14, 2023

In this post, we will help you quickly level up your overall knowledge of data pipeline architecture by reviewing: Table of Contents What is data pipeline architecture? Why is data pipeline architecture important? This is frequently referred to as a 5 or 7 layer (depending on who you ask) data stack like in the image below.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

Knowledge Hut

JUNE 27, 2023

For example, it can enable remote access to patient records in healthcare, provide online learning platforms for education, and offer affordable data storage & processing in finance. Big Data Overview: Big data refers to the massive volumes of structured and unstructured data generated by modern digital technologies.

Computer Science 52

Computer Science Entertainment Healthcare Finance 52

Snowflake

MARCH 30, 2023

Snowflake can also ingest external tables from on-premise s data sources via S3-compliant data storage APIs. Batch/file-based data is modeled into the raw vault table structures as the hub, link, and satellite tables illustrated at the beginning of this post.

Engineering 83

Engineering Raw Data Data Science Scala 83

Monte Carlo

AUGUST 31, 2023

Data pipelines can handle both batch and streaming data, and at a high-level, the methods for measuring data quality for either type of asset are much the same. In many ways, the cloud makes data easier to manage, more accessible to a wider variety of users, and far faster to process.

Data Lake 52

Data Lake Data Pipeline Unstructured Data Data Warehouse 52

Knowledge Hut

SEPTEMBER 6, 2023

This is an entry-level database certification, and it is a stepping stone for other role-based data-focused certifications, like Azure Data Engineer Associate, Azure Database Administrator Associate, Azure Developer Associate, or Power BI Data Analyst Associate. Skills acquired : Core data concepts. Data storage options.

Database 52

Database Certification MongoDB MySQL 52

phData: Data Engineering

JANUARY 3, 2022

This means it’s business-critical that companies can derive value from their data to better inform business decisions, protect their enterprise and their customers, and grow their business. This comprehensive guide will cover all of the basics of data engineering including common roles, functions, and responsibilities.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Governance 52

ProjectPro

NOVEMBER 30, 2021

Flat Files: CSV, TXT, and Excel spreadsheets are standard text file formats for storing data. Nontechnical users can easily access these data formats without installing data science software. SQL RDBMS: The SQL database is a trendy data storage where we can load our processed data.

Process 52

Process Data Pipeline Data Warehouse AWS 52

ProjectPro

FEBRUARY 8, 2017

However, this does not mean just Hadoop but Hadoop along with other big data technologies like in-memory frameworks, data marts, discovery tools ,data warehouses and others that are required to deliver the data to the right place at right time. Apache Ranger renders centralized security administration for hadoop clusters.

Big Data 40

Big Data Hadoop Data Lake Data Governance 40

ProjectPro

FEBRUARY 21, 2023

Microsoft introduced the Data Engineering on Microsoft Azure DP 203 certification exam in June 2021 to replace the earlier two exams. This professional certificate demonstrates one's abilities to integrate, analyze, and transform various structured and unstructured data for creating effective data analytics solutions.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

AltexSoft

JUNE 26, 2023

Find sources of relevant data. Choose data collection methods and tools. Decide on a sufficient data amount. Set up data storage technology. Below, we’ll elaborate on each step one by one and share our experience of data collection. Key differences between structured, semi-structured, and unstructured data.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

AltexSoft

MAY 12, 2023

In today’s data-driven world, organizations amass vast amounts of information that can unlock significant insights and inform decision-making. A staggering 80 percent of this digital treasure trove is unstructured data, which lacks a pre-defined format or organization. What is unstructured data?

Unstructured Data 59

Unstructured Data NoSQL Hadoop Data Lake 59

AltexSoft

SEPTEMBER 23, 2021

A data hub, in turn, is rather a terminal or distribution station: It collects information only to harmonize it, and sends it to the required end-point systems. Data lake vs data hub. A data lake is quite opposite of a DW, as it stores large amounts of both structured and unstructured data.

Architecture 52

Architecture Data Lake Unstructured Data Data Warehouse 52

AltexSoft

JULY 29, 2022

No matter the actual size, each cluster accommodates three functional layers — Hadoop distributed file systems for data storage, Hadoop MapReduce for processing, and Hadoop Yarn for resource management. Today, Hadoop which combines data storage and processing capabilities remains a basis for many Big Data projects.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

AltexSoft

NOVEMBER 10, 2021

Traditional data warehouse platform architecture. Key data warehouse limitations: Inefficiency and high costs of traditional data warehouses in terms of continuously growing data volumes. Inability to handle unstructured data such as audio, video, text documents, and social media posts. Data lake.

Architecture 52

Architecture Data Lake Data Warehouse Metadata 52

Precisely

FEBRUARY 9, 2024

Reporting standards are also becoming increasingly stringent, and data integrity capabilities help ensure that metrics are clear, accurate, and readily accessible. The ultimate goal of a fabric is to bring together structured and unstructured data and make it useful for humans and machines alike.

Data Integration 69

Data Integration Government Metadata Data 69