AltexSoft

MAY 14, 2021

A growing number of companies now use this data to uncover meaningful insights and improve their decision-making, but they can’t store and process it by the means of traditional data storage and processing units. Key Big Data characteristics. And most of this data has to be handled in real-time or near real-time.

Big Data 78

Big Data Data Analytics IT NoSQL 78

phData: Data Engineering

AUGUST 4, 2023

Data storage is a vital aspect of any Snowflake Data Cloud database. Within Snowflake, data can either be stored locally or accessed from other cloud storage systems. Amazon S3 for AWS, Azure Blob Storage for Azure, or Google Cloud Storage for GCP) to store the actual data files in micro-partitions.

Cloud Storage 52

Cloud Storage Google Cloud Amazon Web Services Data Storage 52

ProjectPro

NOVEMBER 15, 2021

DataFrames are used by Spark SQL to accommodate structured and semi-structured data. You can also access data through non-relational databases such as Apache Cassandra, Apache HBase, Apache Hive, and others like the Hadoop Distributed File System. CMAK is developed to help the Kafka community.

Big Data 52

Big Data Project Metadata Programming Language 52

AltexSoft

MARCH 14, 2023

Additionally, this modularity can help prevent vendor lock-in, giving organizations more flexibility and control over their data stack. Many components of a modern data stack (such as Apache Airflow, Kafka, Spark, and others) are open-source and free. But this distinction has been blurred with the era of cloud data warehouses.

IT 59

IT Data Warehouse Data Governance Data Lake 59

AltexSoft

JULY 29, 2022

No matter the actual size, each cluster accommodates three functional layers — Hadoop distributed file systems for data storage, Hadoop MapReduce for processing, and Hadoop Yarn for resource management. Yet, its pool of supporters definitely stands out if compared with other Big Data platforms. Hadoop ecosystem evolvement.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

ProjectPro

JANUARY 25, 2022

Features of PySpark Features that contribute to PySpark's immense popularity in the industry- Real-Time Computations PySpark emphasizes in-memory processing, which allows it to perform real-time computations on huge volumes of data. PySpark is used to process real-time data with Kafka and Streaming, and this exhibits low latency.

Big Data 52

Big Data Data Process Process Kafka 52

AltexSoft

SEPTEMBER 21, 2023

Data in Elasticsearch is organized into documents, which are then categorized into indices for better search efficiency. Each document is a collection of fields, the basic data units to be searched. Fields in these documents are defined and governed by mappings akin to a schema in a relational database.

Engineering 52

Engineering NoSQL Programming Language Java 52

ProjectPro

FEBRUARY 21, 2023

Knowledge of the definition and architecture of AWS Big Data services and their function in the data engineering lifecycle, including data collection and ingestion, data analytics, data storage, data warehousing, data processing, and data visualization.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

ProjectPro

MAY 31, 2021

Data Description: You will use the Covid-19 dataset(COVID-19 Cases.csv) from data.world , for this project, which contains a few of the following attributes: people_positive_cases_count county_name case_type data_source Language Used: Python 3.7 Big Data Project using Hadoop with Source Code for Web Server Log Processing 5.

Big Data 52

Big Data Coding Project Hadoop 52

ProjectPro

NOVEMBER 22, 2021

Spark saves data in memory (RAM), making data retrieval quicker and faster when needed. Spark is a low-latency computation platform because it offers in-memory data storage and caching. The cache() function or the persist() method with proper persistence settings can be used to cache data.

Hadoop 52

Hadoop Python Datasets Metadata 52