Smart Data Collective

OCTOBER 14, 2019

With big data careers in high demand, the required skillsets will include: Apache Hadoop. Software businesses are using Hadoop clusters on a more regular basis now. Apache Hadoop develops open-source software and lets developers process large amounts of data across different computers by using simple models.

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Pickl AI

SEPTEMBER 19, 2024

Summary: This article compares Spark vs Hadoop, highlighting Spark’s fast, in-memory processing and Hadoop’s disk-based, batch processing model. Introduction Apache Spark and Hadoop are potent frameworks for big data processing and distributed computing. What is Apache Hadoop?

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IBM Journey to AI blog

MARCH 21, 2024

Leveraging distributed storage and processing frameworks such as Apache Hadoop, Spark or Dask accelerates data ingestion, transformation and analysis. Additionally, using in-memory databases and caching mechanisms minimizes latency and improves data access speeds.

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Analytics Vidhya

MAY 5, 2022

The post A Beginners’ Guide to Apache Hadoop’s HDFS appeared first on Analytics Vidhya. This article was published as a part of the Data Science Blogathon. Introduction With a huge increment in data velocity, value, and veracity, the volume of data is growing exponentially with time.

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Precisely

MARCH 9, 2023

Hadoop, Snowflake, Databricks and other products have rapidly gained adoption. We will also address some of the key distinctions between platforms like Hadoop and Snowflake, which have emerged as valuable tools in the quest to process and analyze ever larger volumes of structured, semi-structured, and unstructured data.

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Pickl AI

DECEMBER 2, 2024

Processing frameworks like Hadoop enable efficient data analysis across clusters. This includes structured data (like databases), semi-structured data (like XML files), and unstructured data (like text documents and videos). Key Takeaways Big Data originates from diverse sources, including IoT and social media.

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Pickl AI

NOVEMBER 25, 2024

Processing frameworks like Hadoop enable efficient data analysis across clusters. This includes structured data (like databases), semi-structured data (like XML files), and unstructured data (like text documents and videos). Key Takeaways Big Data originates from diverse sources, including IoT and social media.

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Women in Big Data

OCTOBER 9, 2024

Components of a Big Data Pipeline Data Sources (Collection): Data originates from various sources, such as databases, APIs, and log files. Examples include transactional databases, social media feeds, and IoT sensors. This phase ensures quality and consistency using frameworks like Apache Spark or AWS Glue.

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Smart Data Collective

MAY 20, 2019

With databases, for example, choices may include NoSQL, HBase and MongoDB but its likely priorities may shift over time. For frameworks and languages, there’s SAS, Python, R, Apache Hadoop and many others. But no matter how difficult it is, data analysts must continue to stay at the forefront of that growth.

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Pickl AI

SEPTEMBER 17, 2024

In addition to traditional structured data (like databases), there is a wealth of unstructured and semi-structured data (such as emails, videos, images, and social media posts). This section will highlight key tools such as Apache Hadoop, Spark, and various NoSQL databases that facilitate efficient Big Data management.

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Pickl AI

NOVEMBER 4, 2024

They are responsible for building and maintaining data architectures, which include databases, data warehouses, and data lakes. Data Modelling Data modelling is creating a visual representation of a system or database. Physical Models: These models specify how data will be physically stored in databases.

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Pickl AI

AUGUST 1, 2023

Data Engineers work to build and maintain data pipelines, databases, and data warehouses that can handle the collection, storage, and retrieval of vast amounts of data. Data Storage: Storing the collected data in various storage systems, such as relational databases, NoSQL databases, data lakes, or data warehouses.

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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.

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Pickl AI

JULY 18, 2023

SQL: Mastering Data Manipulation Structured Query Language (SQL) is a language designed specifically for managing and manipulating databases. While it may not be a traditional programming language, SQL plays a crucial role in Data Science by enabling efficient querying and extraction of data from databases.

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Mlearning.ai

MAY 16, 2023

It is used to extract data from various sources, transform the data to fit a specific data model or schema, and then load the transformed data into a target system such as a data warehouse or a database. The speed layer is responsible for processing real-time data and storing it in a temporary database.

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DagsHub

OCTOBER 23, 2024

Data can come from different sources, such as databases or directly from users, with additional sources, including platforms like GitHub, Notion, or S3 buckets. Vector Databases Vector databases help store unstructured data by storing the actual data and its vector representation. mp4,webm, etc.), and audio files (.wav,mp3,acc,

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Mlearning.ai

JULY 10, 2023

In my 7 years of Data Science journey, I’ve been exposed to a number of different databases including but not limited to Oracle Database, MS SQL, MySQL, EDW, and Apache Hadoop. Tables inherent the key characteristics of its platform BigQuery which provides an upper hand over traditional databases.

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Pickl AI

JULY 30, 2024

Below are some prominent use cases for Apache NiFi: Data Ingestion from Diverse Sources NiFi excels at collecting data from various sources, including log files, sensors, databases, and APIs. It can connect to various database s, file systems, and cloud storage solutions, enabling seamless data transfer without significant downtime.

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Pickl AI

AUGUST 21, 2024

Crawlers then store this information in a database for indexing. Structured data can be easily imported into databases or analytical tools. Lead Generation Companies can scrape contact information from websites to build databases of potential customers. This ensures that the indexed information remains current and accurate.

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IBM Journey to AI blog

JANUARY 18, 2023

data platforms and databases), all interacting with one another to provide greater value. A data fabric can consist of multiple data warehouses, data lakes, IoT/Edge devices and transactional databases. Even the transactional databases may also join the fabric network as nodes to offer or consume data assets.

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Data Science Dojo

MAY 24, 2024

Java is also widely used in big data technologies, supported by powerful Java-based tools like Apache Hadoop and Spark, which are essential for data processing in AI. Big Data Technologies With the growth of data-driven technologies, AI engineers must be proficient in big data platforms like Hadoop, Spark, and NoSQL databases.

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