Knowledge Hut

JULY 26, 2023

These datasets typically involve high volume, velocity, variety, and veracity, which are often referred to as the 4 v's of Big Data: Volume: Volume refers to the vast amount of data generated and collected from various sources. Managing and analyzing such large volumes of data requires specialized tools and technologies.

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Knowledge Hut

JANUARY 25, 2024

Let's dive into the top data cleaning techniques and best practices for the future – no mess, no fuss, just pure data goodness! What is Data Cleaning? It involves removing or correcting incorrect, corrupted, improperly formatted, duplicate, or incomplete data. Why Is Data Cleaning So Important?

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Knowledge Hut

APRIL 20, 2023

It involves establishing a framework for data management that ensures data quality, privacy, security, and compliance with regulatory requirements. The mix of people, procedures, technologies, and systems ensures that the data within a company is reliable, safe, and simple for employees to access.

Data Governance 59

Data Governance Government Data Cleanse Data Security 59

Edureka

AUGUST 2, 2023

Their efforts make ensuring that data is accurate, dependable, and consistent, laying the groundwork for data analysis and decision-making. What does a Data Processing Analysts do ? A data processing analyst’s job description includes a variety of duties that are essential to efficient data management.

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Databand.ai

AUGUST 30, 2023

DataOps , short for data operations, is an emerging discipline that focuses on improving the collaboration, integration, and automation of data processes across an organization. These tools help organizations implement DataOps practices by providing a unified platform for data teams to collaborate, share, and manage their data assets.

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Cloudera

JUNE 22, 2023

The top-line benefits of a hybrid data platform include: Cost efficiency. A hybrid data platform enables the preservation of existing investments in legacy applications and workloads without modifying them. It ensures compliance with regulatory requirements while shifting non-sensitive data and workloads to the cloud.

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ProjectPro

FEBRUARY 8, 2023

The role of an ETL developer is to extract data from multiple sources, transform it into a usable format and load it into a data warehouse or any other destination database. ETL developers are the backbone of a successful data management strategy as they ensure that the data is consistent and accurate for data-driven decision-making.

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U-Next

NOVEMBER 18, 2022

AI can help optimize resources, improve efficiency, and reduce the cost of storage and management. Enhanced Data Management . AI in cloud computing has improved data management by leaps and bounds due to the advent of Artificial Intelligence (AI). The introduction of AI in cloud computing is quite disruptive.

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Databand.ai

AUGUST 30, 2023

The DataOps framework is a set of practices, processes, and technologies that enables organizations to improve the speed, accuracy, and reliability of their data management and analytics operations. The core philosophy of DataOps is to treat data as a valuable asset that must be managed and processed efficiently.

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Data Governance Data Pipeline Government Data Cleanse 52

Databand.ai

JULY 19, 2023

However, with the rise of the internet and cloud computing, data is now generated and stored across multiple sources and platforms. This dispersed data environment creates a challenge for businesses that need to access and analyze their data. This can be achieved through data cleansing and data validation.

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Knowledge Hut

FEBRUARY 15, 2023

Further, data analysts tend to transform this customer-driven data into forms that are insightful for business decision-making processes. Data analysis comprises the following elements: Data collection Data screening Data analysis and inspection Interpretation and visualization Data management for future usage.

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Databand.ai

AUGUST 30, 2023

Incorporation of Data Governance and Management Data leaders must enforce proper data governance practices, including metadata management, data cataloging, and lineage tracking, are in place to maintain regulatory compliance, enforce privacy policies, and increase overall trustworthiness of data.

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Databand.ai

JULY 19, 2023

Enhancing Data Quality Data ingestion plays an instrumental role in enhancing data quality. During the data ingestion process, various validations and checks can be performed to ensure the consistency and accuracy of data. Another way data ingestion enhances data quality is by enabling data transformation.

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ProjectPro

JANUARY 27, 2023

If you're wondering how the ETL process can drive your company to a new era of success, this blog will help you discover what use cases of ETL make it a critical component in many data management and analytic systems. Accessing this information lets you engage in profitable stocks and ventures and make better financial decisions.

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Ascend.io

FEBRUARY 28, 2024

Engineers ensure the availability of clean, structured data, a necessity for AI systems to learn from patterns, make accurate predictions, and automate decision-making processes. Through the design and maintenance of efficient data pipelines , data engineers facilitate the seamless flow and accessibility of data for AI processing.

Data Engineering 52

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Knowledge Hut

JANUARY 18, 2024

Together, automation and DataOps are transforming the way businesses approach data analytics, making it faster, more accurate, and more efficient. Data Specialists’ Shortages Will Create Challenges in Data Management The demand for data specialists is rapidly increasing as data volumes continue to grow.

Data Analytics 52

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Databand.ai

JUNE 20, 2023

Data integrity refers to the overall accuracy, consistency, and reliability of data stored in a database, data warehouse, or any other information storage system. It is a critical aspect of data management, ensuring that the information used by an organization is correct, up-to-date, and fit for its intended purpose.

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Edureka

AUGUST 2, 2023

Due to its strong data analysis and manipulation skills, it has significantly increased its prominence in the field of data science. Python offers a strong ecosystem for data scientists to carry out activities like data cleansing, exploration, visualization, and modeling thanks to modules like NumPy, Pandas, and Matplotlib.

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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. The primary distinction lies in the sequence and location of the data transformation process.

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Monte Carlo

MARCH 24, 2023

Integrity is crucial for meeting regulatory requirements, maintaining user confidence, and preventing data breaches or loss. How Do You Maintain Data Integrity? Data integrity issues can arise at multiple points across the data pipeline. Learn more in our blog post Data Validity: 8 Clear Rules You Can Use Today.

Data Validation 52

Data Validation Data Integration Data Cleanse Data Pipeline 52

Databand.ai

AUGUST 30, 2023

Accelerated decision-making In today’s fast-paced business environment, where decisions need to be made quickly based on accurate information, having access to reliable and trustworthy data becomes crucial. IBM® Databand® is a powerful and comprehensive data testing tool that offers a wide range of features and functions.

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Knowledge Hut

JANUARY 30, 2024

Whether it's aggregating customer interactions, analyzing historical sales trends, or processing real-time sensor data, data extraction initiates the process. Utilizes structured data or datasets that may have already undergone extraction and preparation. Primary Focus Structuring and preparing data for further analysis.

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phData: Data Engineering

AUGUST 4, 2023

Data storage is a vital aspect of any Snowflake Data Cloud database. Within Snowflake, data can either be stored locally or accessed from other cloud storage systems. Snowflake stores and manages data in the cloud using a shared disk approach, which simplifies data management.

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Cloud Storage Google Cloud Amazon Web Services Data Storage 52

Knowledge Hut

SEPTEMBER 26, 2023

The insights derived from the data in hand are then turned into impressive business intelligence visuals such as graphs or charts for the executive management to make strategic decisions. In this post, we will discuss the top power BI developer skills required to access Microsoft’s power business intelligence software.

BI 52

BI Business Intelligence Raw Data Data Analysis 52

AltexSoft

MARCH 2, 2023

Data quality involves storing data in its correct and consistent form. Here’s a deep dive into data quality management and tools. Data availability is responsible for making data accessible to appropriate personnel within the system. The DAMA DMBOK wheel.

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RandomTrees

NOVEMBER 29, 2023

To do this the data driven approach that today’s company’s employ must be more adaptable and susceptible to change because if the EDW/BI systems fails to provide this, how will the change in information be addressed.? DaaS involves supplying data from a wide variety of sources through API and on demand designed for simplifying data access.

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AltexSoft

AUGUST 29, 2023

If your organization fits into one of these categories and you’re considering implementing advanced data management and analytics solutions, keep reading to learn how data lakes work and how they can benefit your business. Subsequent transformation into a more analyzable form occurs within the data lake itself.

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Data Lake Architecture IT Amazon Web Services 52

RandomTrees

SEPTEMBER 6, 2020

The architecture is three layered: Database Storage: Snowflake has a mechanism to reorganize the data into its internal optimized, compressed and columnar format and stores this optimized data in cloud storage. This stage handles all the aspects of data storage like organization, file size, structure, compression, metadata, statistics.

Cloud Storage 52

Cloud Storage Data Ingestion Data Cleanse Data Warehouse 52

AltexSoft

MAY 14, 2021

What is Big Data analytics? Big Data analytics is the process of finding patterns, trends, and relationships in massive datasets that can’t be discovered with traditional data management techniques and tools. The best way to understand the idea behind Big Data analytics is to put it against regular data analytics.

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Knowledge Hut

NOVEMBER 28, 2023

Hence, learning and developing the required data engineer skills set will ensure a better future and can even land you better salaries in good companies anywhere in the world. After all, data engineer skills are required to collect data, transform it appropriately, and make it accessible to data scientists.

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Knowledge Hut

OCTOBER 13, 2023

Data cleaning involves removing all the unwanted data from the data set and keeping only the data that is relevant to your analysis. Remove duplicate data to avoid misrepresentation of the analysis Eliminate irrelevant data columns or rows Fix structural errors like inconsistent data formats, data types, etc.

BI 52

BI Business Analyst Datasets Raw Data 52

U-Next

OCTOBER 25, 2022

Format consistency: To make better business decisions, it is necessary for users to have access to unified, organized data. . Historical overview: It’s important to have access to the data of previous projects so as to make quick decisions about deadline projections, team productivity, allocation of budgets, etc.,

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Netflix Tech

MARCH 25, 2019

The ingestions (ETL) pipelines transform enriched datasets to a common data model (design based on a graph structure stored as vertices and edges) to serve lineage use cases. We are loading the lineage data to a graph database to enable seamless integration with a REST data lineage service to address business use cases.

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U-Next

SEPTEMBER 9, 2022

Why Are Data Science Professionals so Highly Demanded Worldwide? . Data Abundance: Organizations worldwide are struggling to manage the massive volumes of data to their access, and managing future databases that will become exponentially greater presents an even bigger difficulty. . Machine Learning .

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Knowledge Hut

FEBRUARY 7, 2023

Spatial data is any form of data that helps us directly or indirectly reference a specific location or geographical area on the surface of the earth or elsewhere. Geographic Information systems, or GIS, is the most common method of processing and analyzing spatial data. Spatial data is stored as shapely data.

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DataKitchen

SEPTEMBER 20, 2021

As discussed earlier, data professionals spend over half of their time on operational execution. Think of your data operations workflows as a series of pipeline steps. For example, data cleansing, ETL, running a model, or even provisioning cloud infrastructure. Security vault providing access to tools.

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ProjectPro

JUNE 18, 2015

For Silicon Valley startups launching a big data platform, the best way to reduce expenses is to pay remote workers so that they can distribute tasks to people who have internet access anywhere in the world. Thus, companies might not be willing hire employees for data management tasks paving way for crowdsourcing in big data ventures.

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