Data Science Dojo

OCTOBER 31, 2024

The rise of big data technologies and the need for data governance further enhance the growth prospects in this field. Machine Learning Engineer Description Machine Learning Engineers are responsible for designing, building, and deploying machine learning models that enable organizations to make data-driven decisions.

Data Science 195

Data Science Data Scientist Machine Learning Machine Learning 195

Data Science Dojo

JANUARY 12, 2023

It can process any type of data, regardless of its variety or magnitude, and save it in its original format. Hadoop systems and data lakes are frequently mentioned together. However, instead of using Hadoop, data lakes are increasingly being constructed using cloud object storage services.

Data Lakes 221

Data Lakes Data Warehouse Hadoop Machine Learning 221

Dataconomy

FEBRUARY 23, 2023

Data engineers play a crucial role in managing and processing big data. They are responsible for designing, building, and maintaining the infrastructure and tools needed to manage and process large volumes of data effectively. They must also ensure that data privacy regulations, such as GDPR and CCPA , are followed.

Big Data 91

Big Data Big Data Data Engineer Data Engineering 91

Pickl AI

JULY 29, 2024

Summary: A Hadoop cluster is a collection of interconnected nodes that work together to store and process large datasets using the Hadoop framework. Introduction A Hadoop cluster is a group of interconnected computers, or nodes, that work together to store and process large datasets using the Hadoop framework.

Hadoop 52

Hadoop Clustering Big Data Big Data 52

Pickl AI

SEPTEMBER 17, 2024

Summary: This blog delves into the multifaceted world of Big Data, covering its defining characteristics beyond the 5 V’s, essential technologies and tools for management, real-world applications across industries, challenges organisations face, and future trends shaping the landscape.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Pickl AI

DECEMBER 2, 2024

Summary: Big Data encompasses vast amounts of structured and unstructured data from various sources. Key components include data storage solutions, processing frameworks, analytics tools, and governance practices. Key Takeaways Big Data originates from diverse sources, including IoT and social media.

Big Data 52

Big Data Big Data Data Lakes Apache Hadoop 52

Pickl AI

AUGUST 9, 2024

Summary: A comprehensive Big Data syllabus encompasses foundational concepts, essential technologies, data collection and storage methods, processing and analysis techniques, and visualisation strategies. Fundamentals of Big Data Understanding the fundamentals of Big Data is crucial for anyone entering this field.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Pickl AI

NOVEMBER 25, 2024

Summary: Big Data encompasses vast amounts of structured and unstructured data from various sources. Key components include data storage solutions, processing frameworks, analytics tools, and governance practices. Key Takeaways Big Data originates from diverse sources, including IoT and social media.

Big Data 52

Big Data Big Data Data Lakes Apache Hadoop 52

Pickl AI

SEPTEMBER 11, 2024

Summary: Big Data as a Service (BDaaS) offers organisations scalable, cost-effective solutions for managing and analysing vast data volumes. By outsourcing Big Data functionalities, businesses can focus on deriving insights, improving decision-making, and driving innovation while overcoming infrastructure complexities.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Smart Data Collective

APRIL 24, 2023

Big data has led to some huge changes in the way we live. John Deighton is a leading expert on big data technology. His research focuses on the importance of data in the online world. Innovations in the early 20th century changed how data could be used. Deighton studies how this evolution came to be.

Big Data 119

Big Data Big Data Hadoop Azure 119

Smart Data Collective

MAY 20, 2019

We’re well past the point of realization that big data and advanced analytics solutions are valuable — just about everyone knows this by now. Big data alone has become a modern staple of nearly every industry from retail to manufacturing, and for good reason. The Rise of Regulation.

Analytics 111

Analytics Analytics Data Analyst Machine Learning 111

Precisely

DECEMBER 20, 2022

Read more > #4 4 Real-World Examples of Financial Institutions Making Use of Big Data Big data has moved beyond “new tech” status and into mainstream use. Within the financial industry, there are some specialized uses for data integration and big data analytics.

Data Governance 52

Data Governance Data Quality Big Data Big Data 52

ODSC - Open Data Science

APRIL 28, 2023

But before AI/ML can contribute to enterprise-level transformation, organizations must first address the problems with the integrity of the data driving AI/ML outcomes. The truth is, companies need trusted data, not just big data. That’s why any discussion about AI/ML is also a discussion about data integrity.

ML 98

ML ML Data Silos Data Quality 98

Precisely

MARCH 9, 2023

As cloud computing platforms make it possible to perform advanced analytics on ever larger and more diverse data sets, new and innovative approaches have emerged for storing, preprocessing, and analyzing information. Hadoop, Snowflake, Databricks and other products have rapidly gained adoption. They can be changed, but not easily.

Data Warehouse 59

Data Warehouse Data Lakes Hadoop Big Data 59

ODSC - Open Data Science

SEPTEMBER 27, 2023

In the ever-evolving world of big data, managing vast amounts of information efficiently has become a critical challenge for businesses across the globe. Unlike traditional data warehouses or relational databases, data lakes accept data from a variety of sources, without the need for prior data transformation or schema definition.

Data Lakes 98

Data Lakes Data Warehouse Database Big Data 98

Pickl AI

NOVEMBER 4, 2024

Introduction Data Engineering is the backbone of the data-driven world, transforming raw data into actionable insights. As organisations increasingly rely on data to drive decision-making, understanding the fundamentals of Data Engineering becomes essential. What is Data Engineering? million by 2028.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

IBM Journey to AI blog

JULY 5, 2023

The challenges of a monolithic data lake architecture Data lakes are, at a high level, single repositories of data at scale. Data may be stored in its raw original form or optimized into a different format suitable for consumption by specialized engines. Data governance remains an unexplored frontier for this technology.

Data Lakes 78

Data Lakes Data Warehouse Data Governance Analytics 78

Pickl AI

JULY 25, 2023

They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. With expertise in programming languages like Python , Java , SQL, and knowledge of big data technologies like Hadoop and Spark, data engineers optimize pipelines for data scientists and analysts to access valuable insights efficiently.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Pickl AI

JANUARY 12, 2025

Moreover, regulatory requirements concerning data utilisation, like the EU’s General Data Protection Regulation GDPR, further complicate the situation. Such challenges can be mitigated by durable data governance, continuous training, and high commitment toward ethical standards.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence AI AI 52

Pickl AI

OCTOBER 10, 2024

Enhanced Data Quality : These tools ensure data consistency and accuracy, eliminating errors often occurring during manual transformation. Scalability : Whether handling small datasets or processing big data, transformation tools can easily scale to accommodate growing data volumes.

Data Quality 52

Data Quality AWS Machine Learning Machine Learning 52

DataRobot Blog

OCTOBER 3, 2017

With this integration, customers can now harness the full power of Azure’s Big Data offerings in a self-service manner to gain immediate value.”. This highlights the two companies’ shared vision on self-service data discovery with an emphasis on collaboration and data governance.

Data Lakes 52

Data Lakes Azure Data Pipeline Hadoop 52

Pickl AI

JULY 30, 2024

Its architecture includes FlowFiles, repositories, and processors, enabling efficient data processing and transformation. With a user-friendly interface and robust features, NiFi simplifies complex data workflows and enhances real-time data integration.

ETL 52

ETL Data Lakes Big Data Big Data 52

Alation

FEBRUARY 20, 2020

As big data matures, the way you think about it may have to shift also. It’s no longer enough to build the data warehouse. Dave Wells, analyst with the Eckerson Group suggests that realizing the promise of the data warehouse requires a paradigm shift in the way we think about data along with a change in how we access and use it.

Data Warehouse 52

Data Warehouse Data Lakes Hadoop Data Preparation 52

DagsHub

AUGUST 23, 2024

We already know that a data quality framework is basically a set of processes for validating, cleaning, transforming, and monitoring data. Data Governance Data governance is the foundation of any data quality framework. It primarily caters to large organizations with complex data environments.

Data Quality 52

Data Quality Data Governance Machine Learning Machine Learning 52

Pickl AI

APRIL 2, 2024

Understanding Data Structured Data: Organized data with a clear format, often found in databases or spreadsheets. Unstructured Data: Data without a predefined structure, like text documents, social media posts, or images. Data Cleaning: Process of identifying and correcting errors or inconsistencies in datasets.

Data Science 52

Data Science Machine Learning Machine Learning Predictive Analytics 52

Pickl AI

NOVEMBER 15, 2023

Data scientists can explore, experiment, and derive valuable insights without the constraints of a predefined structure. This capability empowers organizations to uncover hidden patterns, trends, and correlations in their data, leading to more informed decision-making.

Data Lakes 52

Data Lakes Data Warehouse Database ETL 52

Pickl AI

NOVEMBER 5, 2024

Tableau supports many data sources, including cloud databases, SQL databases, and Big Data platforms. Users can connect to live data or extract data for analysis, giving flexibility to those with extensive and complex datasets. This makes it adaptable for industries with strict data governance policies.

Power BI 52

Power BI Tableau Business Intelligence Business Intelligence 52

DagsHub

OCTOBER 23, 2024

Data Lakes Data lakes are centralized repositories designed to store vast amounts of raw, unstructured, and structured data in their native format. They enable flexible data storage and retrieval for diverse use cases, making them highly scalable for big data applications.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

Alation

JANUARY 25, 2022

This is a key component of active data governance. These capabilities are also key for a robust data fabric. Another key nuance of a data fabric is that it captures social metadata. Social metadata captures the associations that people create with the data they produce and consume. The Power of Social Metadata.

DataOps 52

DataOps SQL ML ML 52

Data Science Blog

MARCH 14, 2023

Big Data tauchte als Buzzword meiner Recherche nach erstmals um das Jahr 2011 relevant in den Medien auf. Big Data wurde zum Business-Sprech der darauffolgenden Jahre. In der Parallelwelt der ITler wurde das Tool und Ökosystem Apache Hadoop quasi mit Big Data beinahe synonym gesetzt.

Big Data 147

Big Data Big Data Apache Hadoop Data Science 147

Data Science Blog

MAY 15, 2023

Darüber hinaus können Data Governance- und Sicherheitsrichtlinien auf die Daten in einem Data Lakehouse angewendet werden, um die Datenqualität und die Einhaltung von Vorschriften zu gewährleisten. Wenn Ihre Analyse jedoch eine gewisse Latenzzeit tolerieren kann, könnte ein Data Warehouse die bessere Wahl sein.

Data Warehouse 147

Data Warehouse Data Lakes Azure AWS 147