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. Disruptive Trend #1: Hadoop. More than any other advancement in analytic systems over the last 10 years, Hadoop has disrupted data ecosystems. Introducing Integration with Kylo.

Data Lakes 52

Data Lakes Hadoop Tableau Big Data 52

Becoming Human

JANUARY 23, 2023

- a beginner question Let’s start with the basic thing if I talk about the formal definition of Data Science so it’s like “Data science encompasses preparing data for analysis, including cleansing, aggregating, and manipulating the data to perform advanced data analysis” , is the definition enough explanation of data science?

Data Science 105

Data Science Big Data Big Data Deep Learning 105

O'Reilly Media

MAY 15, 2025

All the big companies have their own definitions. Id set the stage with my definition: a system that can take action on your half. 23:19 : Definitely. 29:29 : Back then, we only had a few options: Hadoop, Spark. 30:03 : Back then people didnt need Hadoop or MapReduce or Spark if they didnt have lots of data.

AI 74

AI AI Hadoop Python 74

Alation

FEBRUARY 20, 2020

Alation does a deep parse of the usage logs on the database tables or Hadoop structures that power a MicroStrategy visualization to capture how often those assets have been used and who in the organization are the experts on that resource. Those experts can then use Alation’s collaborative curation features to further enrich the catalog.

Data Governance 52

Data Governance Tableau Hadoop Data Pipeline 52

ODSC - Open Data Science

SEPTEMBER 27, 2023

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. Understanding Data Lakes A data lake is a centralized repository that stores structured, semi-structured, and unstructured data in its raw format.

Data Lakes 98

Data Lakes Data Warehouse Database Big Data 98

AWS Machine Learning Blog

MAY 16, 2024

Problem definition Traditionally, the recommendation service was mainly provided by identifying the relationship between products and providing products that were highly relevant to the product selected by the customer. xlarge","Name":"Master Instance Group"},{"InstanceCount":2,"InstanceGroupType":"CORE","InstanceType":"r5.xlarge","Name":"Core

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Pickl AI

AUGUST 1, 2023

Hadoop: The Definitive Guide by Tom White This comprehensive guide delves into the Apache Hadoop ecosystem, covering HDFS, MapReduce, and big data processing. Key Benefits & Takeaways: Master Python’s data processing capabilities, making you proficient in data cleaning, wrangling, and exploration.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

IBM Journey to AI blog

JULY 5, 2023

In the case of Hadoop, one of the more popular data lakes, the promise of implementing such a repository using open-source software and having it all run on commodity hardware meant you could store a lot of data on these systems at a very low cost. Ability to update data (ACID properties) and support transactional concurrency.

Data Lakes 78

Data Lakes Data Warehouse Data Governance Analytics 78

ODSC - Open Data Science

JUNE 1, 2023

The bit that I’ve highlighted in bold is the most important part of the definition in my opinion. It can ingest from batch data sources (such as Hadoop HDFS, Amazon S3, and Google Cloud Storage) as well as stream data sources (such as Apache Kafka and Redpanda). Pinot stores data in tables, each of which must first define a schema.

Analytics 98

Analytics Analytics Apache Kafka Data Science 98

Pickl AI

JUNE 7, 2024

Definition and Explanation of the ETL Process ETL is a data integration method that combines data from multiple sources. Key Features Out-of-the-Box Connectors: Includes connectors for databases like Hadoop, CRM systems, XML, JSON, and more. Cost Considerations: Implementing and maintaining Hadoop clusters can incur significant costs.

ETL 40

ETL Data Quality Data Pipeline Data Warehouse 40

Pickl AI

MAY 29, 2024

Definition of Data Science Data Science involves collecting, analysing, and interpreting data to gain valuable insights and knowledge. Big Data Technologies (Hadoop, Spark) Hadoop and Spark are super helpful for managing big data. On the other hand, data warehousing requires building and managing large data repositories.

Data Scientist 52

Data Scientist Data Science Machine Learning Machine Learning 52

DagsHub

OCTOBER 23, 2024

For instance, if you are working with several high-definition videos, storing them would take a lot of storage space, which could be costly. Popular data lake solutions include Amazon S3 , Azure Data Lake , and Hadoop. So, when working with unstructured data in an AI/ML project, you must consider storage space.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

IBM Journey to AI blog

JANUARY 18, 2023

It can include technologies that range from Oracle, Teradata and Apache Hadoop to Snowflake on Azure, RedShift on AWS or MS SQL in the on-premises data center, to name just a few. As it is clear from the definition above, unlike data fabric, data mesh is about analytical data. All phases of the data-information lifecycle.

Data Lakes 52

Data Lakes Data Warehouse Azure Apache Hadoop 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

Alation

JANUARY 25, 2022

As a reminder, here’s Gartner’s definition of data fabric: “A design concept that serves as an integrated layer (fabric) of data and connecting processes. In this blog, we will focus on the “integrated layer” part of this definition by examining each of the key layers of a comprehensive data fabric in more detail. ” 1.

DataOps 52

DataOps SQL ML ML 52

Pickl AI

MAY 31, 2023

Big Data Technologies: As the amount of data grows, familiarity with big data technologies such as Apache Hadoop, Apache Spark, and distributed computer platforms might be useful. It is critical for knowing how to work with huge data sets efficiently. Also Read: How to become a Data Scientist after 10th? How long will it take to learn Python?

Data Science 52

Data Science Python Data Scientist Machine Learning 52

The MLOps Blog

MAY 31, 2023

You may also like Building a Machine Learning Platform [Definitive Guide] Consideration for data platform Setting up the Data Platform in the right way is key to the success of an ML Platform. 2 It also helps to standardize feature definitions across teams. How to set up a data processing platform?

ML 59

ML ML Algorithm Machine Learning 59

Data Science Blog

MARCH 14, 2023

In der Parallelwelt der ITler wurde das Tool und Ökosystem Apache Hadoop quasi mit Big Data beinahe synonym gesetzt. 2 Denn heute spielt die Definition darüber, was Big Data eigentlich genau ist, wirklich keine Rolle mehr. Big Data tauchte als Buzzword meiner Recherche nach erstmals um das Jahr 2011 relevant in den Medien auf.

Big Data 147

Big Data Big Data Apache Hadoop Data Science 147

Data Science Blog

MAY 15, 2023

Der Artikel beginnt mit einer Definition, was ein Lakehouse ist, gibt einen kurzen geschichtlichen Abriss, wie das Lakehouse entstanden ist und zeigt, warum und wie man ein Data Lakehouse aufbauen sollte. Aber Moment mal, was ist eigentlich ein Data Lakehouse? Apache Iceberg ist auf AWS, Azure und Google Cloud Platform verfügbar.

Data Warehouse 147

Data Warehouse Data Lakes Azure AWS 147

Dataconomy

MAY 20, 2025

Definition and purpose The primary purpose of ensemble modeling is to combine multiple predictive models to maximize accuracy and minimize error rates. Technological advancements Improvements in big data technologies, such as Hadoop and Spark, have transformed the landscape of ensemble modeling.

Decision Trees 91

Decision Trees Predictive Analytics Machine Learning Machine Learning 91