ODSC - Open Data Science

MAY 31, 2023

Be sure to check out his talk, “ Apache Kafka for Real-Time Machine Learning Without a Data Lake ,” there! The combination of data streaming and machine learning (ML) enables you to build one scalable, reliable, but also simple infrastructure for all machine learning tasks using the Apache Kafka ecosystem.

Data Lakes 131

Data Lakes Machine Learning Machine Learning Apache Kafka 131

Precisely

JANUARY 9, 2025

Data marts soon evolved as a core part of a DW architecture to eliminate this noise. Data marts involved the creation of built-for-purpose analytic repositories meant to directly support more specific business users and reporting needs (e.g., financial reporting, customer analytics, supply chain management).

Data Warehouse 119

Data Warehouse Hadoop Data Lakes Data Governance 119

Data Science Dojo

JULY 6, 2023

Google BigQuery: Google BigQuery is a serverless, cloud-based data warehouse designed for big data analytics. It offers scalable storage and compute resources, enabling data engineers to process large datasets efficiently. It provides a scalable and fault-tolerant ecosystem for big data processing.

Data Engineering 195

Data Engineering Data Engineering Data Engineering Data Engineer 195

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. As data lakes gain prominence as a preferred solution for storing and processing enormous datasets, the need for effective data version control mechanisms becomes increasingly evident.

Data Lakes 98

Data Lakes Data Warehouse Database Big Data 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.

Data Warehouse 59

Data Warehouse Data Lakes Hadoop Big Data 59

Data Science Blog

MAY 15, 2023

tl;dr Ein Data Lakehouse ist eine moderne Datenarchitektur, die die Vorteile eines Data Lake und eines Data Warehouse kombiniert. Die Definition eines Data Lakehouse Ein Data Lakehouse ist eine moderne Datenspeicher- und -verarbeitungsarchitektur, die die Vorteile von Data Lakes und Data Warehouses vereint.

Data Warehouse 147

Data Warehouse Data Lakes Azure AWS 147

Alation

FEBRUARY 20, 2020

Architecturally the introduction of Hadoop, a file system designed to store massive amounts of data, radically affected the cost model of data. Organizationally the innovation of self-service analytics, pioneered by Tableau and Qlik, fundamentally transformed the user model for data analysis.

Data Lakes 52

Data Lakes Hadoop Tableau Big Data 52

AWS Machine Learning Blog

FEBRUARY 21, 2025

Thats why we use advanced technology and data analytics to streamline every step of the homeownership experience, from application to closing. Model training and scoring was performed either from Jupyter notebooks or through jobs scheduled by Apaches Oozie orchestration tool, which was part of the Hadoop implementation.

Data Science 68

Data Science AWS Hadoop Data Scientist 68

ODSC - Open Data Science

JUNE 7, 2023

To make your data management processes easier, here’s a primer on data lakes, and our picks for a few data lake vendors worth considering. What is a data lake? First, a data lake is a centralized repository that allows users or an organization to store and analyze large volumes of data.

Data Lakes 52

Data Lakes Azure Data Warehouse Hadoop 52

IBM Journey to AI blog

SEPTEMBER 19, 2023

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

Data Science 94

Data Science Analytics Analytics Data Scientist 94

phData

NOVEMBER 8, 2024

The most used open table formats currently are Apache Iceberg, Delta Lake, and Apache Hudi. These systems are built on open standards and offer immense analytical and transactional processing flexibility. Adopting an Open Table Format architecture is becoming indispensable for modern data systems. Why are They Essential?

Data Lakes 52

Data Lakes Data Warehouse Database Azure 52

Pickl AI

NOVEMBER 15, 2023

Discover the nuanced dissimilarities between Data Lakes and Data Warehouses. Data management in the digital age has become a crucial aspect of businesses, and two prominent concepts in this realm are Data Lakes and Data Warehouses. It acts as a repository for storing all the data.

Data Lakes 52

Data Lakes Data Warehouse Database ETL 52

Pickl AI

JULY 6, 2023

Here comes the role of Hive in Hadoop. Hive is a powerful data warehousing infrastructure that provides an interface for querying and analyzing large datasets stored in Hadoop. In this blog, we will explore the key aspects of Hive Hadoop. What is Hadoop ? Thus ensuring optimal performance.

Hadoop 52

Hadoop SQL Big Data Big Data 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. Artificial Intelligence (AI) ersetzt.

Big Data 147

Big Data Big Data Apache Hadoop Data Science 147

Dataconomy

FEBRUARY 25, 2025

Data storage and processing The storage and processing of big data require specific architectures tailored to handle large volumes and various types of data efficiently. Data lakes Data lakes provide a centralized repository for storing raw data in its original format, making it easy to analyze different data types as needed.

Big Data 91

Big Data Big Data Data Lakes Machine Learning 91

Smart Data Collective

MARCH 21, 2022

More case studies are added every day and give a clear hint – data analytics are all set to change, again! . Data Management before the ‘Mesh’. In the early days, organizations used a central data warehouse to drive their data analytics.

Data Lakes 75

Data Lakes Hadoop Data Silos Data Warehouse 75

Flipboard

NOVEMBER 17, 2023

About the authors Igor Alekseev is a Senior Partner Solution Architect at AWS in Data and Analytics domain. Prior joining AWS, as a Data/Solution Architect he implemented many projects in Big Data domain, including several data lakes in Hadoop ecosystem.

K-nearest Neighbors 170

K-nearest Neighbors AWS Clustering Database 170

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

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

Women in Big Data

OCTOBER 9, 2024

A traditional data pipeline is a structured process that begins with gathering data from various sources and loading it into a data warehouse or data lake. Once ingested, the data is prepared through filtering, error correction, and restructuring for ease of use.

Big Data 59

Big Data Big Data Apache Kafka Data Pipeline 59

Pickl AI

SEPTEMBER 17, 2024

The importance of Big Data lies in its potential to provide insights that can drive business decisions, enhance customer experiences, and optimise operations. Organisations can harness Big Data Analytics to identify trends, predict outcomes, and make informed decisions that were previously unattainable with smaller datasets.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

IBM Journey to AI blog

JANUARY 18, 2023

In another decade, the internet and mobile started the generate data of unforeseen volume, variety and velocity. It required a different data platform solution. Hence, Data Lake emerged, which handles unstructured and structured data with huge volume. All phases of the data-information lifecycle.

Data Lakes 52

Data Lakes Data Warehouse Azure 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. Velocity It indicates the speed at which data is generated and processed, necessitating real-time analytics capabilities.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

AWS Machine Learning Blog

DECEMBER 18, 2023

Among all, the native time series capabilities is a standout feature, making it ideal for a managing high volume of time-series data, such as business critical application data, telemetry, server logs and more. With efficient querying, aggregation, and analytics, businesses can extract valuable insights from time-stamped data.

Clustering 120

Clustering AWS Database ML 120

Pickl AI

NOVEMBER 4, 2024

Data Engineering is designing, constructing, and managing systems that enable data collection, storage, and analysis. It involves developing data pipelines that efficiently transport data from various sources to storage solutions and analytical tools. ETL is vital for ensuring data quality and integrity.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

Pickl AI

JANUARY 28, 2025

Research indicates that companies utilizing advanced analytics are 5 times more likely to make faster decisions than their competitors. Key Components of Business Intelligence Architecture Business Intelligence (BI) architecture is a structured framework that enables organizations to gather, analyze, and present data effectively.

Business Intelligence 52

Business Intelligence Business Intelligence ETL Data Lakes 52

Alation

FEBRUARY 20, 2020

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. Self-service analytics environments are giving rise to the data marketplace.

Data Warehouse 52

Data Warehouse Data Lakes Hadoop Data Preparation 52

Pickl AI

JULY 30, 2024

IoT Data Processing With the rise of the Internet of Things (IoT), NiFi is increasingly used to process data generated by IoT devices. It can handle data streams from sensors, perform real-time analytics, and route the data to appropriate storage solutions or analytics platforms.

ETL 52

ETL Data Lakes Big Data Big Data 52

Pickl AI

APRIL 6, 2023

Data Engineering is one of the most productive job roles today because it imbibes both the skills required for software engineering and programming and advanced analytics needed by Data Scientists. How to Become an Azure Data Engineer? Data Warehousing concepts and knowledge should be strong. What is Polybase?

Azure 52

Azure Data Engineering Data Engineering Data Engineering 52

DagsHub

OCTOBER 23, 2024

To combine the collected data, you can integrate different data producers into a data lake as a repository. A central repository for unstructured data is beneficial for tasks like analytics and data virtualization. Data Cleaning The next step is to clean the data after ingesting it into the data lake.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

The MLOps Blog

JANUARY 23, 2023

These tools may have their own versioning system, which can be difficult to integrate with a broader data version control system. For instance, our data lake could contain a variety of relational and non-relational databases, files in different formats, and data stored using different cloud providers. DVC Git LFS neptune.ai

ML 52

ML ML Data Lakes Machine Learning 52

Alation

FEBRUARY 13, 2020

.” Part of GoDaddy’s transformation was to get the right customer data consolidated in one place and make it accessible to every employee for data-driven decision making. This meant a large Hadoop deployment, self-service analytics tools available to every employee with Tableau, and a data catalog from Alation.

Decision Science 59

Decision Science Analytics Analytics Data Science 59

DagsHub

NOVEMBER 11, 2024

Video : Movies, live streams, and CCTV footage combine visual and audio data, making them highly complex. Video analytics enable object detection, motion tracking, and behavioural analysis for security, traffic monitoring, or customer engagement insights.

AI 52

AI AI Data Lakes Database 52

Precisely

JANUARY 19, 2023

Data Integration Enterprises are betting big on analytics, and for good reason. The volume, velocity, and variety of data is growing exponentially. Platforms like Hadoop and Spark prompted many companies to begin thinking about big data differently than they had in the past.

AWS 52

AWS Cloud Computing Data Pipeline Big Data 52

Pickl AI

OCTOBER 10, 2024

Summary: Data transformation tools streamline data processing by automating the conversion of raw data into usable formats. These tools enhance efficiency, improve data quality, and support Advanced Analytics like Machine Learning. BI tools rely on high-quality, consistent data to generate accurate insights.

Data Quality 52

Data Quality AWS Machine Learning Machine Learning 52

Pickl AI

JULY 8, 2024

Definition and Explanation of Data Pipelines A data pipeline is a series of interconnected steps that ingest raw data from various sources, process it through cleaning, transformation, and integration stages, and ultimately deliver refined data to end users or downstream systems.

Data Pipeline 52

Data Pipeline Data Quality Database Apache Kafka 52

phData

AUGUST 9, 2023

ELT enables access to raw data in the warehouse, powers a DevOps-based style of data integration, and taps into the parallel processing power of modern cloud-based data platforms. In short, ELT exemplifies the data strategy required in the era of big data, cloud, and agile analytics.

ETL 52

ETL Data Warehouse Cloud Data Big Data 52