Smart Data Collective

SEPTEMBER 23, 2020

While there is a lot of discussion about the merits of data warehouses, not enough discussion centers around data lakes. We talked about enterprise data warehouses in the past, so let’s contrast them with data lakes. Both data warehouses and data lakes are used when storing big data.

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Data Lakes Data Warehouse Big Data Big Data 110

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 Engineer Data Engineering 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.

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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.

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Data Lakes Data Warehouse Hadoop Apache Hadoop 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.

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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.

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Data Lakes Hadoop Tableau Big Data 52

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.

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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.

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Data Science Analytics Analytics Data Scientist 94

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.

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Hadoop SQL Big Data Big Data 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.

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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.

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Big Data Big Data Apache Hadoop Data Science 147

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.

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K-nearest Neighbors AWS Clustering Database 170

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.

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Data Lakes Hadoop Data Silos Data Warehouse 72

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.

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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.

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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.

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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.

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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.

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Data Lakes Data Warehouse Azure Apache Hadoop 52

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?

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Data Lakes Data Warehouse Database Azure 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.

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Clustering AWS Database ML 131

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.

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Big Data Big Data Big Data Analytics Big Data Analytics 52

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.

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Data Engineering Data Engineering Data Engineer Data Engineering 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.

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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.

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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?

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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.

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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

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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.

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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.

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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.

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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.

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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.

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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.

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ETL Data Warehouse Cloud Data Big Data 52

Dataconomy

FEBRUARY 23, 2023

Data engineers are responsible for designing and building the systems that make it possible to store, process, and analyze large amounts of data. These systems include data pipelines, data warehouses, and data lakes, among others. However, building and maintaining these systems is not an easy task.

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Big Data Big Data Data Engineering Data Engineering 91

phData

FEBRUARY 5, 2024

Replicate can interact with a wide variety of databases, data warehouses, and data lakes (on-premise or based in the cloud). Closing Migrating to a new data warehousing platform can be a challenging endeavor. Get to know all the ins and outs of your upcoming migration. We have you covered !

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SQL Database Data Quality Data Warehouse 52

IBM Journey to AI blog

JULY 5, 2023

Data Lakes have been around for well over a decade now, supporting the analytic operations of some of the largest world corporations. Such data volumes are not easy to move, migrate or modernize. The challenges of a monolithic data lake architecture Data lakes are, at a high level, single repositories of data at scale.

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Data Lakes Data Warehouse Data Governance Analytics 78

phData

SEPTEMBER 20, 2023

phData has been working in data engineering since the inception of the company back in 2015. We have seen customers transform their data analytics with Snowflake and transform their data engineering and machine learning applications with Spark, Java, Scala, and Python.

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SQL Python Data Lakes Machine Learning 98