Knowledge Hut

SEPTEMBER 26, 2023

Organisations are constantly looking for robust and effective platforms to manage and derive value from their data in the constantly changing landscape of data analytics and processing. These platforms provide strong capabilities for data processing, storage, and analytics, enabling companies to fully use their data assets.

Data Lake 52

Data Lake Database-centric Machine Learning Pipeline-centric 52

Knowledge Hut

APRIL 23, 2024

Big Data vs Small Data: Volume Big Data refers to large volumes of data, typically in the order of terabytes or petabytes. It involves processing and analyzing massive datasets that cannot be managed with traditional data processing techniques. Small Data is collected and processed at a slower pace.

Big Data 52

Big Data Datasets Data Analysis Media 52

The Modern Data Company

MAY 8, 2023

It can store any type of data — structured, unstructured, and semi-structured — in its native format, providing a highly scalable and adaptable solution for diverse data needs. Data is stored in a schema-on-write approach, which means data is cleaned, transformed, and structured before storing.

Data Management 52

Data Management Management Data Lake Data Governance 52

The Modern Data Company

MAY 8, 2023

It can store any type of data — structured, unstructured, and semi-structured — in its native format, providing a highly scalable and adaptable solution for diverse data needs. Data is stored in a schema-on-write approach, which means data is cleaned, transformed, and structured before storing.

Data Management 52

Data Management Management Data Lake Data Governance 52

The Modern Data Company

MAY 8, 2023

It can store any type of data — structured, unstructured, and semi-structured — in its native format, providing a highly scalable and adaptable solution for diverse data needs. Data is stored in a schema-on-write approach, which means data is cleaned, transformed, and structured before storing.

Data Management 52

Data Management Management Data Lake Data Governance 52

Snowflake

AUGUST 19, 2024

For example: Ingest performance: We improved the ingest performance of both JSON and Parquet files with case-insensitive data up to 25%. .* Automatic Clustering, Materialized Views and Search Optimization are major examples of this, and they all accelerate your queries via intelligent data-processing techniques.

Data Ingestion 52

Data Ingestion BI Structured Data Engineering 52

AltexSoft

MARCH 30, 2023

What is Databricks Databricks is an analytics platform with a unified set of tools for data engineering, data management , data science, and machine learning. It combines the best elements of a data warehouse, a centralized repository for structured data, and a data lake used to host large amounts of raw data.

Scala 64

Scala Data Lake Machine Learning BI 64

Data Engineering Weekly

NOVEMBER 20, 2022

[link] The short YouTube video gives a nice overview of the Data Cards. We often think of AI/ ML as a complex data processing problem, but it doesn’t make any use until it is exposed to an end user or an application. Daniel Buschek: What makes user interfaces intelligent? So what makes a user interface intelligent?

Data Engineering 52

Data Engineering Data Engineer Engineering Datasets 52

Databand.ai

JULY 17, 2023

MLOps: Key Similarities and Differences Similarities between DataOps and MLOps Focus on collaboration: Both methodologies emphasize the importance of cross-functional teams working together to improve data processes, including data scientists, engineers, analysts, and business stakeholders.

Data Pipeline 52

Data Pipeline Machine Learning High Quality Data Data Ingestion 52

ProjectPro

OCTOBER 28, 2015

Getting data into the Hadoop cluster plays a critical role in any big data deployment. Data ingestion is important in any big data project because the volume of data is generally in petabytes or exabytes. Sqoop in Hadoop is mostly used to extract structured data from databases like Teradata, Oracle, etc.,

ETL Tools 52

ETL Tools Hadoop Relational Database Unstructured Data 52

Ascend.io

JUNE 8, 2023

Acting as the core infrastructure, data pipelines include the crucial steps of data ingestion, transformation, and sharing. Data Ingestion Data in today’s businesses come from an array of sources, including various clouds, APIs, warehouses, and applications.

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 52

Knowledge Hut

APRIL 25, 2023

Database management: Data engineers should be proficient in storing and managing data and working with different databases, including relational and NoSQL databases. Data modeling: Data engineers should be able to design and develop data models that help represent complex data structures effectively.

Data Engineering 52

Data Engineering Data Engineer Engineering Google Cloud 52

AltexSoft

AUGUST 29, 2023

Data sources can be broadly classified into three categories. Structured data sources. These are the most organized forms of data, often originating from relational databases and tables where the structure is clearly defined. Semi-structured data sources. Video explaining how data streaming works.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Towards Data Science

MARCH 5, 2024

The storage system is using Capacitor, a proprietary columnar storage format by Google for semi-structured data and the file system underneath is Colossus, the distributed file system by Google. Load data For data ingestion Google Cloud Storage is a pragmatic way to solve the task. Also this query comes at 0 costs.

Bytes 97

Bytes Google Cloud Cloud Storage Utilities 97

Cloudera

JANUARY 22, 2019

Today’s data landscape is characterized by exponentially increasing volumes of data, comprising a variety of structured, unstructured, and semi-structured data types originating from an expanding number of disparate data sources located on-premises, in the cloud, and at the edge. Data orchestration.

Big Data 44

Big Data NoSQL Hadoop Data Lake 44

Striim

OCTOBER 11, 2024

While legacy ETL has a slow transformation step, modern ETL platforms, like Striim, have evolved to replace disk-based processing with in-memory processing. This advancement allows for real-time data transformation , enrichment, and analysis, providing faster and more efficient data processing.

Data Pipeline 52

Data Pipeline MongoDB Unstructured Data Data Lake 52

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. Big Data analytics processes and tools.

Big Data 78

Big Data Data Analytics IT NoSQL 78

Silectis

JANUARY 3, 2021

BI (Business Intelligence) Strategies and systems used by enterprises to conduct data analysis and make pertinent business decisions. Big Data Large volumes of structured or unstructured data. Big Query Google’s cloud data warehouse. Data Warehouse A storage system used for data analysis and reporting.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

Monte Carlo

JUNE 14, 2023

Why is data pipeline architecture important? 5 Data pipeline architecture designs and their evolution The Hadoop era , roughly 2011 to 2017, arguably ushered in big data processing capabilities to mainstream organizations. Despite Hadoop’s parallel and distributed processing, compute was a limited resource as well.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

ProjectPro

JANUARY 31, 2023

There are three steps involved in the deployment of a big data model: Data Ingestion: This is the first step in deploying a big data model - Data ingestion, i.e., extracting data from multiple data sources. Data Processing: This is the final step in deploying a big data model.

Big Data 52

Big Data Hadoop Relational Database AWS 52

ProjectPro

DECEMBER 7, 2021

It can also consist of simple or advanced processes like ETL (Extract, Transform and Load) or handle training datasets in machine learning applications. In broader terms, two types of data -- structured and unstructured data -- flow through a data pipeline. Step 1- Automating the Lakehouse's data intake.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

Rockset

JULY 29, 2022

This fast, serverless, highly scalable, and cost-effective multi-cloud data warehouse has built-in machine learning, business intelligence, and geospatial analysis capabilities for querying massive amounts of structured and semi-structured data. The Snowpipe feature manages continuous data ingestion.

Data Analytics 52

Data Analytics Data Warehouse Datasets Cloud 52

ProjectPro

AUGUST 24, 2021

Data Engineering Project for Beginners If you are a newbie in data engineering and are interested in exploring real-world data engineering projects, check out the list of data engineering project examples below. This big data project discusses IoT architecture with a sample use case.

Data Engineering 52

Data Engineering Data Engineer Coding Project 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

Knowledge Hut

APRIL 23, 2024

Gathering data at high velocities necessitates capturing and ingesting data streams as they occur, ensuring timely acquisition and availability for analysis. Utilizing is related to the data processing and analyzing speed for gleaning useful insights. Customer data come in numerous formats.

Big Data 52

Big Data Media Datasets Unstructured Data 52

Monte Carlo

APRIL 24, 2023

A notebook-based environment allows data engineers, data scientists, and analysts to work together seamlessly, streamlining data processing, model development, and deployment. Databricks also pioneered the modern data lakehouse architecture, which combines the best of data lakes and data warehouses.

Data Lake 52

Data Lake Google Cloud Data Warehouse AWS 52

ProjectPro

DECEMBER 28, 2021

Discretized Streams, or DStreams, are fundamental abstractions here, as they represent streams of data divided into small chunks(referred to as batches). The raw event data can be converted into structured data collected using a continuous ETL pipeline based on Kafka, Spark Streaming, and HDFS.

Architecture 52

Architecture Kafka Java Scala 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. To learn more about the recent updates and contribute: [link] 8.

Big Data 52

Big Data Project Metadata Programming Language 52

ProjectPro

NOVEMBER 22, 2021

MapReduce Apache Spark Only batch-wise data processing is done using MapReduce. Apache Spark can handle data in both real-time and batch mode. The data is stored in HDFS (Hadoop Distributed File System), which takes a long time to retrieve. PySpark Data Science Interview Questions Q1. Discuss PySpark SQL in detail.

Hadoop 52

Hadoop Python Datasets Metadata 52

ProjectPro

AUGUST 21, 2015

Big Data Hadoop Interview Questions and Answers These are Hadoop Basic Interview Questions and Answers for freshers and experienced. Hadoop vs RDBMS Criteria Hadoop RDBMS Datatypes Processes semi-structured and unstructured data. Processes structured data. are all examples of unstructured data.

Hadoop 40

Hadoop Big Data Metadata Java 40

phData: Data Engineering

JULY 18, 2023

First up, let’s dive into the foundation of every Modern Data Stack, a cloud-based data warehouse. Central Source of Truth for Analytics A Cloud Data Warehouse (CDW) is a type of database that provides analytical data processing and storage capabilities within a cloud-based infrastructure.

Data Warehouse 52

Data Warehouse Pipeline-centric Government Data 52

Data Engineering Weekly

JUNE 4, 2023

link] Uber: Spark Analysers: Catching Anti-Patterns In Spark Apps One of the challenges in commoditizing data processing engines like Spark is that it requires an expert user to understand and operate this system. Many of the real-world data, all the way from medical images to astro monitoring, are unstructured data.

Data Engineering 52

Data Engineering Data Engineer Engineering Medical 52

AltexSoft

JULY 29, 2022

It relieves the MapReduce engine of scheduling tasks and decouples data processing from resource management. To facilitate data ingestion, there are Apache Flume aggregating log data from multiple servers and Apache Sqoop designed to transport information between Hadoop and relational (SQL) databases.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

ProjectPro

JULY 22, 2016

Data lake is gaining momentum across various organizations and everyone wants to know how to implement a data lake and why. There are several people writing that data lakes are replacing data warehouses but this is just another technology hype that is coming across the effective use of data.

Data Lake 40

Data Lake Data Warehouse Hadoop Unstructured Data 40

ProjectPro

MAY 31, 2021

The project develops a data processing chain in a big data environment using Amazon Web Services (AWS) cloud tools, including steps like dimensionality reduction and data preprocessing and implements a fruit image classification engine. Machines and humans are both sources of structured data.

Big Data 52

Big Data Coding Project Hadoop 52

ProjectPro

OCTOBER 20, 2021

Experiment to see what works best for your data, automate it using pipelines, and then monitor the performance of the workflow. Data: Data Engineering Pipelines Data is everything. Make sure that the quality of data works for your use case.

Machine Learning 52

Machine Learning Algorithm Data Science Government 52

RandomTrees

MAY 6, 2025

However, to succeed, AI requires a foundation of reliable and structured data. Modern data engineering can help with this. It creates the systems and processes needed to gather, clean, transfer, and prepare data for AI models. Without it, AI technologies wouldn’t have access to high-quality data.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Cleanse 52