Data Engineering Podcast

FEBRUARY 9, 2020

Summary Designing the structure for your data warehouse is a complex and challenging process. As businesses deal with a growing number of sources and types of information that they need to integrate, they need a data modeling strategy that provides them with flexibility and speed.

Data Lake 100

Data Lake Data Warehouse Hadoop NoSQL 100

Cloudera

DECEMBER 11, 2020

This is what really stood out about the finalists of the Data Security and Governance category. These customers have embedded security and governance throughout their entire data and analytics lifecycle by design. It established a data governance framework within its enterprise data lake.

Data Lake 98

Data Lake Government Data Security Unstructured Data 98

Hepta Analytics

FEBRUARY 14, 2022

We already had a script that downloaded a csv file, processed the data and pushed the data to postgres database. This week, we got to think about our data ingestion design. When the business intelligence needs change, they can go query the raw data again. The purpose of the data is not determined.

Data Ingestion 52

Data Ingestion Data Engineering Data Engineer Engineering 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. We’ll take a closer look at variables that can impact your data next. What is a decentralized data architecture?

Data Lake 52

Data Lake Data Pipeline Unstructured Data Data Warehouse 52

Rockset

APRIL 26, 2023

In part 1 , we covered the technology landscape for real-time analytics on streaming data. In this post, we’ll get to the details: how does one design an RTA system? Offline feature store : Detecting anomalies requires historical data in order to have a baseline for comparisons. The database has two primary jobs.

Architecture 52

Architecture Transportation Data Lake Insurance 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

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

Knowledge Hut

OCTOBER 30, 2023

Data Modeling: Support the development of data models by creating or maintaining simple data relationships and hierarchies. Report and Dashboard Design: Assist in creating basic reports and dashboards using Power BI Desktop. Help in designing layouts and adding visual elements like charts, tables, and filters.

BI 52

BI Business Intelligence Data Cleanse Business Analyst 52

ProjectPro

AUGUST 11, 2021

“Data Lake vs Data Warehouse = Load First, Think Later vs Think First, Load Later” The terms data lake and data warehouse are frequently stumbled upon when it comes to storing large volumes of data. Data Warehouse Architecture What is a Data lake?

Data Lake 52

Data Lake Data Warehouse Cloud Hadoop 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. Different vendors offering data warehouses, data lakes, and now data lakehouses all offer their own distinct advantages and disadvantages for data teams to consider.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

Ascend.io

NOVEMBER 21, 2023

Extract The initial stage of the ELT process is the extraction of data from various source systems. This phase involves collecting raw data from the sources, which can range from structured data in SQL or NoSQL servers, CRM and ERP systems, to unstructured data from text files, emails, and web pages.

Raw Data 52

Raw Data Data Warehouse Data Cleanse Data Integration 52

Snowflake

NOVEMBER 2, 2023

Over the years, the technology landscape for data management has given rise to various architecture patterns, each thoughtfully designed to cater to specific use cases and requirements. In keeping up with ever-evolving data management needs, we’re announcing new capabilities that support customers across all of these patterns.

Data Lake 88

Data Lake Data Warehouse Cloud Unstructured Data 88

Silectis

JANUARY 3, 2021

Data Catalog An organized inventory of data assets relying on metadata to help with data management. Data Engineering Data engineering is a process by which data engineers make data useful. Data Integration Combining data from various, disparate sources into one unified view.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

Knowledge Hut

JANUARY 19, 2024

By delving into the realm of data science, you're not just learning skills; you're gaining a passport to a world of opportunities. From honing your statistical prowess to mastering programming and algorithms, our program is meticulously designed to empower you with the arsenal needed to excel in this field.

Data Science 52

Data Science Machine Learning Datasets Algorithm 52

Knowledge Hut

DECEMBER 26, 2023

Downey and Ben Lauwens) Beginning Julia Programming: For Engineers and Scientists (Author: Sandeep Nagar) Hands-On Design Patterns and Best Practices with Julia: Proven solutions to common problems in software design for Julia 1.x Some reliable books you can refer to on this subject are listed below:-.

Data Science 52

Data Science Scala Programming Language R (Programming) 52

Monte Carlo

SEPTEMBER 6, 2022

Our biggest priority at Monte Carlo is to make the lives of our customers easier by reducing data downtime and helping them accelerate the adoption of reliable data at their companies. These features combine the best of both data lakes and data warehouses into a new type of data store: the data lakehouse.

Data Lake 52

Data Lake Machine Learning Data Science Data 52

Knowledge Hut

DECEMBER 29, 2023

A Data Engineer is someone proficient in a variety of programming languages and frameworks, such as Python, SQL, Scala, Hadoop, Spark, etc. One of the primary focuses of a Data Engineer's work is on the Hadoop data lakes. NoSQL databases are often implemented as a component of data pipelines.

Data Science 98

Data Science Data Mining Deep Learning Programming Language 98

U-Next

SEPTEMBER 7, 2022

The terms “ Data Warehouse ” and “ Data Lake ” may have confused you, and you have some questions. Structuring data refers to converting unstructured data into tables and defining data types and relationships based on a schema. What is Data Warehouse? .

Data Lake 52

Data Lake Data Warehouse Unstructured Data Amazon Web Services 52

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. Data lakes built on NoSQL databases such as Hadoop are the best example of scaled-out data repositories of mixed types. This keeps the data intact.

NoSQL 52

NoSQL SQL Systems PostgreSQL 52

Knowledge Hut

DECEMBER 26, 2023

Certified Financial Planner  Another well-known professional title in the financial planning sector is the Certified Financial Planner designation (CFP). The Certified Financial Planner (CFP) designation, awarded by the Certified Financial Planner Board of Standards, Inc., is one of the most difficult financial counselling programmes.

Banking 52

Banking Insurance Finance Cloud Computing 52

Knowledge Hut

OCTOBER 5, 2023

This article will discover everything you need to know about AWS Big Data Certification and salary. Who is AWS Big Data Specialist? An AWS Big Data Specialist is an IT professional who efficiently designs, analyzes, and maintains data, has experience AWS analytics services, and has cleared the AWS Big Data Specialty certification exam.

Big Data 52

Big Data AWS Certification Amazon Web Services 52

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. What is Azure Synapse?

Data Lake 52

Data Lake Database-centric Pipeline-centric Machine Learning 52

ProjectPro

FEBRUARY 16, 2023

Data engineers use SQL to modify any database and table structure and extract subsets of the data from the database for various business analytics use cases. Furthermore, creating reports from data analysis often involves repeating a process; stored procedures help data engineers overcome this challenge.

Data Engineering 52

Data Engineering Data Engineer SQL Engineering 52

ProjectPro

SEPTEMBER 14, 2016

Concisely, a hadoop developer plays with the data, transforms it, decodes it and ensure that it is not destroyed. Most of the hadoop developers receive unstructured data through flume or structured data through RDBMS and perform data cleaning using various tools in the hadoop ecosystem. to speed up analytics.

Hadoop 40

Hadoop Unstructured Data Java Big Data 40

ProjectPro

SEPTEMBER 26, 2021

Big Data Engineer Salary by Skills The roles and responsibilities of a Big Data Engineer in an organization vary as per the business domain, type of the project, specific big data tools in use, IT infrastructure, technology stack, and a lot more. How much is the salary of a big data engineer?

Big Data 52

Big Data Data Engineering Data Engineer Engineering 52

DareData

JANUARY 30, 2023

Data lakes: These are large-scale data storage systems that are designed to store and process large amounts of raw, unstructured data. Examples of technologies able to aggregate data in data lake format include Amazon S3 or Azure Data Lake. Stanford's Relational Databases and SQL.

Data Engineering 52

Data Engineering Data Engineer NoSQL Engineering 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

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? Zero ETL is a bit of a misnomer.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

AltexSoft

AUGUST 4, 2021

Since data marts provide analytical capabilities for a restricted area of a data warehouse, they offer isolated security and isolated performance. Data mart vs data warehouse vs data lake vs OLAP cube. Data lakes, data warehouses, and data marts are all data repositories of different sizes.

Data Lake 64

Data Lake Data Warehouse ETL Tools Telecommunication 64

Data Engineering Podcast

JUNE 17, 2021

Summary Working with unstructured data has typically been a motivation for a data lake. Kirk Marple has spent years working with data systems and the media industry, which inspired him to build a platform for automatically organizing your unstructured assets to make them more valuable.

Unstructured Data 100

Unstructured Data Data Warehouse Metadata Media 100

Edureka

JUNE 1, 2023

It provides an advanced features to process and analyze the huge amount of data in a day to day world. Why Prefer Cloud for Data Analytics? Cloud technology can be used to build entire data lakes, data warehousing, and data analytics solutions.

AWS 52

AWS Data Analytics Cloud Amazon Web Services 52

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. Note, though, that not any type of web scraping is legal.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 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. What data mesh IS.

Architecture 52

Architecture Data Lake Medical Datasets 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

LinkedIn Engineering

JULY 19, 2023

Open source data lakehouse deployments are built on the foundations of compute engines (like Apache Spark, Trino, Apache Flink), distributed storage (HDFS, cloud blob stores), and metadata catalogs / table formats (like Apache Iceberg, Delta, Hudi, Apache Hive Metastore). While functional, our current setup for managing tables is fragmented.

Big Data 78

Big Data Data Management Management Metadata 78