Knowledge Hut

MARCH 28, 2024

They use many data storage, computation, and analytics technologies to develop scalable and robust data pipelines. Role Level Intermediate Responsibilities Design and develop data pipelines to ingest, process, and transform data. Develop data models, data governance policies, and data integration strategies.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Warehouse 52

Cloudera

MARCH 5, 2024

With built-in features like time travel, schema evolution, and streamlined data discovery, Iceberg empowers data teams to enhance data lake management while upholding data integrity. Available for cloud and now also for the data center. ZDU gives organizations a more convenient means of upgrading.

Data Lake 86

Data Lake Data Storage Government Kafka 86

Databand.ai

AUGUST 30, 2023

DataOps Architecture Legacy data architectures, which have been widely used for decades, are often characterized by their rigidity and complexity. These systems typically consist of siloed data storage and processing environments, with manual processes and limited collaboration between teams.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 52

Snowflake

JUNE 29, 2023

Snowflake is committed to the highest compliance standards needed to help government organizations gain operational advantage, enable better decision-making, and increase efficiency.

Education 52

Education Government Data Lake Data Security 52

Knowledge Hut

NOVEMBER 2, 2023

An Azure Data Engineer is a professional who is in charge of designing, implementing, and maintaining data processing systems and solutions on the Microsoft Azure cloud platform. A Data Engineer is responsible for designing the entire architecture of the data flow while taking the needs of the business into account.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

Edureka

FEBRUARY 7, 2023

They should also be proficient in programming languages such as Python , SQL , and Scala , and be familiar with big data technologies such as HDFS , Spark , and Hive. Learn programming languages: Azure Data Engineers should have a strong understanding of programming languages such as Python , SQL , and Scala.

Data Engineering 52

Data Engineering Data Engineer Engineering Programming Language 52

Knowledge Hut

APRIL 25, 2023

It provides a unified API that allows businesses to collect customer data from various sources, such as websites, mobile apps, and servers. Key features: Centralized customer data Real-time data streaming Support for data governance 6. It can add more processing power and storage as the data grows.

Data Engineering 52

Data Engineering Data Engineer Engineering Google Cloud 52

Monte Carlo

AUGUST 31, 2023

Data pipelines can handle both batch and streaming data, and at a high-level, the methods for measuring data quality for either type of asset are much the same. In many ways, the cloud makes data easier to manage, more accessible to a wider variety of users, and far faster to process.

Data Lake 52

Data Lake Data Pipeline Unstructured Data Data Warehouse 52

Monte Carlo

AUGUST 25, 2023

That’s why it’s essential for teams to choose the right architecture for the storage layer of their data stack. But, the options for data storage are evolving quickly. So let’s get to the bottom of the big question: what kind of data storage layer will provide the strongest foundation for your data platform?

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

Knowledge Hut

JULY 3, 2023

The infrastructure for real-time data ingestion typically consists of several key features: Data Sources: These are the Systems, devices, and applications which create vast amounts of data in real-time. Like IoT devices, sensors, social media platforms, financial data, etc.

Data Ingestion 52

Data Ingestion Pipeline-centric Google Cloud Media 52

ProjectPro

JULY 1, 2016

To meet the growing demands from its customers and provide better user experience – Yahoo implemented cluster management technology YARN Embraced Apache Storm for monitoring clusters and data analyzation. Source - [link] ) Attunity Introduces Hadoop Data Usage Analytics for Growing Data Lake Environments.

Hadoop 40

Hadoop Big Data Data Lake Datasets 40

ProjectPro

FEBRUARY 21, 2023

GCP Data Engineer Certification The Google Cloud Certified Professional Data Engineer certification is ideal for data professionals whose jobs generally involve data governance, data handling, data processing, and performing a lot of feature engineering on data to prepare it for modeling.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

Edureka

FEBRUARY 9, 2023

Azure Data Engineering is a rapidly growing field that involves designing, building, and maintaining data processing systems using Microsoft Azure technologies. As a certified Azure Data Engineer, you have the skills and expertise to design, implement and manage complex data storage and processing solutions on the Azure cloud platform.

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 52

Databand.ai

JULY 19, 2023

ELT offers a solution to this challenge by allowing companies to extract data from various sources, load it into a central location, and then transform it for analysis. The ELT process relies heavily on the power and scalability of modern data storage systems. The data is loaded as-is, without any transformation.

Data Cleanse 52

Data Cleanse Raw Data Data Storage Data Warehouse 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. This feature allows for a more flexible exploration of data.

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. This feature allows for a more flexible exploration of data.

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. This feature allows for a more flexible exploration of data.

Data Management 52

Data Management Data Lake Management Data Governance 52

Precisely

JANUARY 25, 2024

Today’s cloud systems excel at high-volume data storage, powerful analytics, AI, and software & systems development. Cloud-based DevOps provides a modern, agile environment for developing and maintaining applications and services that interact with the organization’s mainframe data. Best Practice 5.

Metadata 69

Metadata Data Governance Relational Database Government 69

DareData

JANUARY 30, 2023

In this post, we'll discuss some key data engineering concepts that data scientists should be familiar with, in order to be more effective in their roles. These concepts include concepts like data pipelines, data storage and retrieval, data orchestrators or infrastructure-as-code.

Data Engineering 52

Data Engineering Data Engineer NoSQL Engineering 52

Snowflake

JANUARY 22, 2024

In 2020, Fidelity kicked off its digital transformation and established an Enterprise Data Lake (EDL) along with Data Labs. All of Fidelity’s storage abides by its data security framework and data governance policies, and provides a holistic approach to metadata management.

Engineering 61

Engineering Data Lake Unstructured Data Metadata 61

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? These pipelines differ from traditional ELT pipelines by doing the data cleaning and normalization prior to load.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

phData: Data Engineering

JANUARY 3, 2022

This means it’s business-critical that companies can derive value from their data to better inform business decisions, protect their enterprise and their customers, and grow their business. This comprehensive guide will cover all of the basics of data engineering including common roles, functions, and responsibilities.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Governance 52

AltexSoft

NOVEMBER 10, 2021

The pun being obvious, there’s more to that than just a new term: Data lakehouses combine the best features of both data lakes and data warehouses and this post will explain this all. What is a data lakehouse? Data warehouse vs data lake vs data lakehouse: What’s the difference.

Architecture 52

Architecture Data Lake Data Warehouse Metadata 52

AltexSoft

JUNE 26, 2023

Find sources of relevant data. Choose data collection methods and tools. Decide on a sufficient data amount. Set up data storage technology. Below, we’ll elaborate on each step one by one and share our experience of data collection. The difference between data warehouses, lakes, and marts.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

Knowledge Hut

NOVEMBER 7, 2023

You can swiftly provision infrastructure services like computation, storage, and databases, as well as machine learning, the internet of things, data lakes and analytics, and much more. " Instead of relying on nearby hard drives and personal data centers, it requires storing and accessing data on distant servers.

Cloud Computing 98

Cloud Computing Cloud Amazon Web Services Entertainment 98

AltexSoft

SEPTEMBER 23, 2021

One of the innovative ways to address this problem is to build a data hub — a platform that unites all your information sources under a single umbrella. This article explains the main concepts of a data hub, its architecture, and how it differs from data warehouses and data lakes. What is Data Hub?

Architecture 52

Architecture Data Lake Unstructured Data Data Warehouse 52

ProjectPro

FEBRUARY 8, 2017

In 2017, big data platforms that are just built only for hadoop will fail to continue and the ones that are data and source agnostic will survive. Organizations are embarking on data lake strategy for applications that are centralized and for applications coming together on a single central platform.

Big Data 40

Big Data Hadoop Data Lake Data Governance 40

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

Snowflake

MARCH 30, 2023

Based on Tecton blog So is this similar to data engineering pipelines into a data lake/warehouse? Snowflake can also ingest external tables from on-premise s data sources via S3-compliant data storage APIs. Yes, feature stores are part of the MLOps discipline.

Engineering 79

Engineering Raw Data Data Science Scala 79

ProjectPro

NOVEMBER 15, 2021

It was built from the ground up for interactive analytics and can scale to the size of Facebook while approaching the speed of commercial data warehouses. Presto allows you to query data stored in Hive, Cassandra, relational databases, and even bespoke data storage.

Big Data 52

Big Data Project Metadata Programming Language 52

ProjectPro

JANUARY 31, 2022

It also offers a unique architecture that allows users to quickly build tables and begin querying data without administrative or DBA involvement. Snowflake is a cloud-based data platform that provides excellent manageability regarding data warehousing, data lakes, data analytics, etc. What Does Snowflake Do?

Architecture 52

Architecture IT Data Warehouse Amazon Web Services 52

Cloudera

OCTOBER 26, 2017

Check out Greg Rahn’s session, “ Rethinking data marts in the cloud: Common architectural patterns for analytics ” at the Strata Data Conference in Singapore, December 4-7, 2017, to learn how to architect analytic workloads in the cloud and the core elements of data governance. Long-lived compute-only clusters.

Cloud 42

Cloud BI Cloud Storage Business Intelligence 42

ProjectPro

NOVEMBER 30, 2021

Flat Files: CSV, TXT, and Excel spreadsheets are standard text file formats for storing data. Nontechnical users can easily access these data formats without installing data science software. SQL RDBMS: The SQL database is a trendy data storage where we can load our processed data.

Process 52

Process Data Pipeline Data Warehouse AWS 52

AltexSoft

MAY 12, 2023

Unstructured data , on the other hand, is unpredictable and has no fixed schema, making it more challenging to analyze. Without a fixed schema, the data can vary in structure and organization. For example, developers can use Twitter API to access and collect public tweets, user profiles, and other data from the Twitter platform.

Unstructured Data 59

Unstructured Data NoSQL Hadoop Data Lake 59

Monte Carlo

JANUARY 12, 2024

On the data lake side, Databricks has launched Unity Catalog to help bring more metadata, structure, and governance to data assets. Real-time data/insights Being able to access real-time data for analysis might sound like overkill to some, but that’s just no longer the case.

Big Data 59

Big Data Data Analytics Data Science Data Lake 59

Monte Carlo

AUGUST 19, 2023

We’ll cover: What is a data platform? To make things a little easier, I’ve outlined the six must-have layers you need to include in your data platform and the order in which many of the best teams choose to implement them. The five must-have layers of a modern data platform Second to “how do I build my data platform?”,

Building 52

Building BI Data Lake Data Governance 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