Knowledge Hut

SEPTEMBER 26, 2023

These platforms provide strong capabilities for data processing, storage, and analytics, enabling companies to fully use their data assets. Microsoft Azure's Azure Synapse, formerly known as Azure SQL Data Warehouse, is a complete analytics offering. Azure Synapse is designed primarily for data warehousing and analytics.

Data Lake 52

Data Lake Database-centric Pipeline-centric Machine Learning 52

AltexSoft

AUGUST 29, 2023

The goal is to provide a comprehensive guide that can be a navigational tool for all specialists plotting their course in today’s data-driven world. What is a data lake? A data lake is a centralized repository designed to hold vast volumes of data in its native, raw format — be it structured, semi-structured, or unstructured.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

phData: Data Engineering

SEPTEMBER 19, 2023

This data can be structured, semi-structured, or unstructured and comes from various sources such as databases, IoT devices, log files, etc. What are Data Modeling Methodologies, and Why Are They Important for a Data Lake?

Data Lake 52

Data Lake Process Metadata Data Warehouse 52

AltexSoft

MAY 12, 2023

What is unstructured data? Definition and examples Unstructured data , in its simplest form, refers to any data that does not have a pre-defined structure or organization. It can come in different forms, such as text documents, emails, images, videos, social media posts, sensor data, etc.

Unstructured Data 59

Unstructured Data NoSQL Hadoop Data Lake 59

AltexSoft

JUNE 26, 2023

Read our article on Hotel Data Management to have a full picture of what information can be collected to boost revenue and customer satisfaction in hospitality. While all three are about data acquisition, they have distinct differences. Key differences between structured, semi-structured, and unstructured data.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

The Modern Data Company

MAY 8, 2023

Data lakes, data warehouses, data hubs, data lakehouses, and data operating systems are data management and storage solutions designed to meet different needs in data analytics, integration, and processing. However, data warehouses can experience limitations and scalability challenges.

Data Management 52

Data Management Data Lake Management Data Governance 52

The Modern Data Company

MAY 8, 2023

Data lakes, data warehouses, data hubs, data lakehouses, and data operating systems are data management and storage solutions designed to meet different needs in data analytics, integration, and processing. However, data warehouses can experience limitations and scalability challenges.

Data Management 52

Data Management Data Lake Management Data Governance 52

The Modern Data Company

MAY 8, 2023

Data lakes, data warehouses, data hubs, data lakehouses, and data operating systems are data management and storage solutions designed to meet different needs in data analytics, integration, and processing. However, data warehouses can experience limitations and scalability challenges.

Data Management 52

Data Management Data Lake Management Data Governance 52

RandomTrees

SEPTEMBER 6, 2020

Lot of cloud-based data warehouses are available in the market today, out of which let us focus on Snowflake. Snowflake is an analytical data warehouse that is provided as Software-as-a-Service (SaaS). Built on new SQL database engine, it provides a unique architecture designed for the cloud.

Cloud Storage 52

Cloud Storage Data Ingestion Data Cleanse Data Warehouse 52

Monte Carlo

JUNE 14, 2023

In this post, we will help you quickly level up your overall knowledge of data pipeline architecture by reviewing: Table of Contents What is data pipeline architecture? Why is data pipeline architecture important? What is data pipeline architecture? They also recently acquired Apache Flink , another streaming solution.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 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

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 BI Google Cloud 64

Data Engineering Weekly

NOVEMBER 20, 2022

With Upsolver SQLake, you build a pipeline for data in motion simply by writing a SQL query defining your transformation. The blog narrates the requirements and motivation to design Janus and critical pieces of the architecture, such as data catalogs, pipeline modeling, pipeline deployment, and pipeline execution.

Data Engineering 52

Data Engineering Data Engineer Engineering Datasets 52

Snowflake

MARCH 30, 2023

3EJHjvm Once a business need is defined and a minimal viable product ( MVP ) is scoped, the data management phase begins with: Data ingestion: Data is acquired, cleansed, and curated before it is transformed. Feature engineering: Data is transformed to support ML model training. ML workflow, ubr.to/3EJHjvm

Engineering 83

Engineering Raw Data Data Science Scala 83

Towards Data Science

MARCH 5, 2024

Summary ∘ Embrace data modeling best practices ∘ Master data operations for cost-effectiveness ∘ Design for efficiency and avoid unnecessary data persistence Disclaimer : BigQuery is a product which is constantly being developed, pricing might change at any time and this article is based on my own experience.

Bytes 67

Bytes Google Cloud Cloud Storage Utilities 67

Knowledge Hut

JANUARY 18, 2024

Let us now look into the differences between AI and Data Science: Data Science vs Artificial Intelligence [Comparison Table] SI Parameters Data Science Artificial Intelligence 1 Basics Involves processes such as data ingestion, analysis, visualization, and communication of insights derived.

Data Science 98

Data Science Deep Learning Business Analyst Data Mining 98

Knowledge Hut

APRIL 25, 2023

The tremendous growth in data generation, then the rise in data engineer jobs - there’s no arguing the fact that the big data industry is at its best pace and you, as an aspiring data engineer, have a lot to learn and make out of it - including some tools! While they go about it - enter big data data engineer tools.

Data Engineering 52

Data Engineering Data Engineer Engineering Google Cloud 52

Rockset

OCTOBER 4, 2022

Both offer SQL support and are capable of ingesting streaming data from Kafka. Storage Format While Rockset and ClickHouse are both designed for analytic applications, there are significant differences in their approaches. Rockset is designed solely for the cloud and is offered as a fully managed cloud service.

MySQL 52

MySQL Kafka Aggregated Data Architecture 52

Rockset

SEPTEMBER 8, 2021

At Rockset, we work hard to build developer tools (as well as APIs and SDKs) that allow you to easily consume semi-structured data using SQL and run sub-second queries on real-time data. Ben Rogojan Ben has spent his career focused on all forms of data. He's always sharing helpful tidbits about data engineering !

SQL 52

SQL Data Ingestion Consulting Data Pipeline 52

ProjectPro

DECEMBER 7, 2021

In broader terms, two types of data -- structured and unstructured data -- flow through a data pipeline. The structured data comprises data that can be saved and retrieved in a fixed format, like email addresses, locations, or phone numbers. What is a Big Data Pipeline?

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 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

Built around a cloud data warehouse, data lake, or data lakehouse. Modern data stack tools are designed to integrate seamlessly with cloud data warehouses such as Redshift, Bigquery, and Snowflake, as well as data lakes or even the child of the first two — a data lakehouse.

IT 59

IT Data Warehouse Data Governance Data Lake 59

Rockset

JULY 6, 2022

This is the fifth post in a series by Rockset's CTO and Co-founder Dhruba Borthakur on Designing the Next Generation of Data Systems for Real-Time Analytics. And the same risk of data errors and data downtime also exists. This keeps the data intact. NoSQL Comes to the Rescue.

NoSQL 52

NoSQL SQL Systems PostgreSQL 52

Rockset

MARCH 27, 2019

You have complex, semi-structured data—nested JSON or XML, for instance, containing mixed types, sparse fields, and null values. It's messy, you don't understand how it's structured, and new fields appear every so often. Organizations will typically build hard-to-maintain ETL pipelines to feed data into their SQL systems.

Raw Data 40

Raw Data SQL NoSQL Datasets 40

Silectis

JANUARY 3, 2021

Data Engineering Data engineering is a process by which data engineers make data useful. Data engineers design, build, and maintain data pipelines that transform data from a raw state to a useful one, ready for analysis or data science modeling. Database A collection of structured data.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

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 AWS Relational Database 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

ProjectPro

DECEMBER 6, 2016

Pentaho published a whitepaper titled “Hadoop and the Analytic Data Pipeline” that highlights the key categories which need to be focused on - Big Data Ingestion, Transformation, Analytics, Solutions. Source: [link] ) How Trifacta is helping data wranglers in Hadoop, the cloud, and beyond.Zdnet.com, November 4,2016.

Hadoop 52

Hadoop Data Lake BI Big Data 52

Rockset

SEPTEMBER 3, 2020

In addition, offloading reads to PostgreSQL might not be the best option when the data you’re dealing with is highly unstructured. In this case, you’ll again end up replicating the absence of structure even in PostgreSQL. Scalability Finally, it’s important to note that PostgreSQL was not designed to be a distributed database.

MongoDB 40

MongoDB PostgreSQL SQL Database 40

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. Hadoop distributed file system or HDFS is a data storage technology designed to handle gigabytes to terabytes or even petabytes of data.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

ProjectPro

MAY 31, 2021

For such scenarios, data-driven integration becomes less comfortable, so you must prefer event-based data integration. This project will teach you how to design and implement an event-based data integration pipeline on the Google Cloud Platform by processing data using DataFlow. Topic Modeling The future is AI!

Big Data 52

Big Data Coding Project Hadoop 52

ProjectPro

JANUARY 25, 2022

Easy Processing- PySpark enables us to process data rapidly, around 100 times quicker in memory and ten times faster on storage. When it comes to data ingestion pipelines, PySpark has a lot of advantages. PySpark allows you to process data from Hadoop HDFS , AWS S3, and various other file systems.

Big Data 52

Big Data Data Process Process Kafka 52

ProjectPro

DECEMBER 28, 2021

Apache Spark Streaming Use Cases There are over 3000 companies that use Spark Streaming including companies like Zendesk, Uber, Netflix, and Pinterest To create real-time telemetry analytics, Uber collects terabytes of event data every day from their mobile users. live logs, IoT device data, system telemetry data, etc.)

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. The bedrock of Apache Spark is Spark Core, which is built on RDD abstraction.

Big Data 52

Big Data Project Metadata Programming Language 52

Netflix Tech

DECEMBER 14, 2018

data access semantics that guarantee repeatable data read behavior for client applications. The following section enumerates the key traits of NMDB and how the design aims to address them. key value stores generally allow storing any data under a key).

Media 94

Media Database Metadata Data Schemas 94

ProjectPro

OCTOBER 20, 2021

BERT (Bidirectional Encoder Representations from Transformers): BERT is designed to pre-train deep bidirectional representations. Similarly, testing AI implementations is not just about unit testing with the appropriate data, but rather designing for all use-cases to deal better when there are unexpected errors.

Machine Learning 52

Machine Learning Algorithm Government Data Science 52

phData: Data Engineering

JULY 18, 2023

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. Enter Snowflake The Snowflake Data Cloud is one of the most popular and powerful CDW providers.

Data Warehouse 52

Data Warehouse Pipeline-centric Government Data 52