AltexSoft

JUNE 26, 2023

From the perspective of data science, all miscellaneous forms of data fall into three large groups: structured, semi-structured, and unstructured. Key differences between structured, semi-structured, and unstructured data. They can be accumulated in NoSQL databases like MongoDB or Cassandra.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

Ascend.io

NOVEMBER 21, 2023

The emergence of cloud data warehouses, offering scalable and cost-effective data storage and processing capabilities, initiated a pivotal shift in data management methodologies. Extract The initial stage of the ELT process is the extraction of data from various source systems.

Raw Data 52

Raw Data Data Warehouse Data Cleanse Data Integration 52

Knowledge Hut

APRIL 25, 2023

Data engineers add meaning to the data for companies, be it by designing infrastructure or developing algorithms. The practice requires them to use a mix of various programming languages, data warehouses, and tools. While they go about it - enter big data data engineer tools.

Data Engineering 52

Data Engineering Data Engineer Engineering Google Cloud 52

Rockset

JULY 28, 2022

MongoDB has grown from a basic JSON key-value store to one of the most popular NoSQL database solutions in use today. This can be used to create data lakes, populate data warehouses or for specific use cases like offloading analytics and text search. Documents in MongoDB can also have complex structures.

MongoDB 52

MongoDB Kafka NoSQL Data Lake 52

AltexSoft

MARCH 14, 2023

As the volume and complexity of data continue to grow, organizations seek faster, more efficient, and cost-effective ways to manage and analyze data. In recent years, cloud-based data warehouses have revolutionized data processing with their advanced massively parallel processing (MPP) capabilities and SQL support.

IT 59

IT Data Warehouse Data Governance Data Lake 59

Silectis

JANUARY 3, 2021

Big Data Processing In order to extract value or insights out of big data, one must first process it using big data processing software or frameworks, such as Hadoop. Big Query Google’s cloud data warehouse. Data Integration Combining data from various, disparate sources into one unified view.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

AltexSoft

MAY 14, 2021

A single car connected to the Internet with a telematics device plugged in generates and transmits 25 gigabytes of data hourly at a near-constant velocity. And most of this data has to be handled in real-time or near real-time. Variety is the vector showing the diversity of Big Data. Data storage and processing.

Big Data 78

Big Data Data Analytics IT NoSQL 78

ProjectPro

JANUARY 24, 2023

With the global cloud data warehousing market likely to be worth $10.42 billion by 2026, cloud data warehousing is now more critical than ever. Cloud data warehouses offer significant benefits to organizations, including faster real-time insights, higher scalability, and lower overhead expenses.

Bytes 52

Bytes Google Cloud Data Warehouse Datasets 52

Edureka

FEBRUARY 7, 2023

One can use polybase: From Azure SQL Database or Azure Synapse Analytics, query data kept in Hadoop, Azure Blob Storage, or Azure Data Lake Store. It does away with the requirement to import data from an outside source. Export information to Azure Data Lake Store, Azure Blob Storage, or Hadoop.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Lake 52

AltexSoft

JULY 19, 2022

In the last few decades, we’ve seen a lot of architectural approaches to building data pipelines , changing one another and promising better and easier ways of deriving insights from information. There have been relational databases, data warehouses, data lakes, and even a combination of the latter two.

Architecture 52

Architecture Data Lake Medical Datasets 52

AltexSoft

OCTOBER 8, 2021

Data integration defines the process of collecting data from a number of disparate source systems and presenting it in a unified form within a centralized location like a data warehouse. So, why is data integration such a big deal? Connections to both data warehouses and data lakes are possible in any case.

Data Integration 52

Data Integration Hadoop Data Warehouse Data Lake 52

AltexSoft

JULY 29, 2022

a runtime environment (sandbox) for classic business intelligence (BI), advanced analysis of large volumes of data, predictive maintenance , and data discovery and exploration; a store for raw data; a tool for large-scale data integration ; and. a suitable technology to implement data lake architecture.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

Knowledge Hut

DECEMBER 29, 2023

Big Data is a part of this umbrella term, which encompasses Data Warehousing and Business Intelligence as well. A Data Engineer's primary responsibility is the construction and upkeep of a data warehouse. They construct pipelines to collect and transform data from many sources.

Data Science 98

Data Science Data Mining Deep Learning Programming Language 98

Rockset

OCTOBER 20, 2021

Until now, the majority of the world’s data transformations have been performed on top of data warehouses, query engines, and other databases which are optimized for storing lots of data and querying them for analytics occasionally. For instance, let’s say you have streaming data coming in from Kafka or Kinesis.

SQL 52

SQL PostgreSQL MongoDB NoSQL 52

ProjectPro

FEBRUARY 16, 2023

In fact, approximately 70% of professional developers who work with data (e.g., data engineer, data scientist , data analyst, etc.) According to the 8,786 data professionals participating in Stack Overflow's survey, SQL is the most commonly-used language in data science. use SQL, compared to 61.7%

Data Engineering 52

Data Engineering Data Engineer SQL Engineering 52

ProjectPro

JULY 27, 2021

Relational Database Management Systems (RDBMS) Non-relational Database Management Systems Relational Databases primarily work with structured data using SQL (Structured Query Language). SQL works on data arranged in a predefined schema. Non-relational databases support dynamic schema for unstructured data.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

ProjectPro

JANUARY 19, 2022

Data engineering is a new and ever-evolving field that can withstand the test of time and computing developments. Companies frequently hire certified Azure Data Engineers to convert unstructured data into useful, structured data that data analysts and data scientists can use.

Data Engineering 52

Data Engineering Data Engineer Engineering Scala 52

ProjectPro

JANUARY 31, 2023

This process involves data collection from multiple sources, such as social networking sites, corporate software, and log files. Data Storage: The next step after data ingestion is to store it in HDFS or a NoSQL database such as HBase. Data Processing: This is the final step in deploying a big data model.

Big Data 52

Big Data Hadoop AWS Relational Database 52

ProjectPro

NOVEMBER 15, 2021

With SQL, machine learning, real-time data streaming, graph processing, and other features, this leads to incredibly rapid big data processing. DataFrames are used by Spark SQL to accommodate structured and semi-structured data. The bedrock of Apache Spark is Spark Core, which is built on RDD abstraction.

Big Data 52

Big Data Project Metadata Programming Language 52

AltexSoft

JULY 27, 2021

The choice of storage depends on the type of data you’re going to use for recommendations in the first place. This can be a standard SQL database for structured data, a NoSQL database for unstructured data, a cloud data warehouse for both, or even a data lake for Big Data projects.

Machine Learning 76

Machine Learning Systems Algorithm Deep Learning 76

ProjectPro

JUNE 4, 2015

Image Credit: slidehshare.net HDFS Use Case- Nokia deals with more than 500 terabytes of unstructured data and close to 100 terabytes of structured data. Nokia uses HDFS for storing all the structured and unstructured data sets as it allows processing of the stored data at a petabyte scale.

Hadoop 52

Hadoop Architecture IT Java 52

ProjectPro

NOVEMBER 14, 2015

Tools/Tech stack used: The tools and technologies used for such weblog trend analysis using Apache Hadoop are NoSql, MapReduce, and Hive. Hadoop Sample Real-Time Project #8 : Facebook Data Analysis Image Source:jovian.ai Business Use Case: The business use case here is to analyze various types of data that are generated on Facebook.

Hadoop 52

Hadoop Project Big Data Healthcare 52

ProjectPro

AUGUST 21, 2015

Hadoop vs RDBMS Criteria Hadoop RDBMS Datatypes Processes semi-structured and unstructured data. Processes structured data. Schema Schema on Read Schema on Write Best Fit for Applications Data discovery and Massive Storage/Processing of Unstructured data. are all examples of unstructured data.

Hadoop 40

Hadoop Big Data Metadata Java 40