Knowledge Hut

DECEMBER 26, 2023

Data Engineers are engineers responsible for uncovering trends in data sets and building algorithms and data pipelines to make raw data beneficial for the organization. This job requires a handful of skills, starting from a strong foundation of SQL and programming languages like Python , Java , etc.

Data Engineering 98

Data Engineering Data Engineer Engineering Pipeline-centric 98

Cloudera

DECEMBER 12, 2018

I would like to start off by asking you to tell us about your background and what kicked off your 20-year career in relational database technology? Greg Rahn: I first got introduced to SQL relational database systems while I was in undergrad. Hi Greg, thank you for joining us today. Michael Moreno: Great.

Data Warehouse 42

Data Warehouse Relational Database Hadoop BI 42

Knowledge Hut

NOVEMBER 17, 2023

Here is a step-by-step guide on how to become an Azure Data Engineer: 1. Understanding SQL You must be able to write and optimize SQL queries because you will be dealing with enormous datasets as an Azure Data Engineer. You should possess a strong understanding of data structures and algorithms.

Data Engineering 52

Data Engineering Data Engineer Engineering Scala 52

Knowledge Hut

NOVEMBER 19, 2023

Machine Learning Integration: Organizations can easily integrate Azure Machine Learning for building predictive models and incorporating machine learning into data engineering workflows. Obtaining the Data Engineer Azure certification is a great way to learn this important tool.

Data Engineering 52

Data Engineering Data Engineer Engineering PostgreSQL 52

Knowledge Hut

JUNE 23, 2023

You should have the expertise to collect data, conduct research, create models, and identify patterns. You should be well-versed with SQL Server, Oracle DB, MySQL, Excel, or any other data storing or processing software. You must develop predictive models to help industries and businesses make data-driven decisions.

Data Engineering 52

Data Engineering Data Engineer Engineering Non-relational Database 52

Edureka

FEBRUARY 9, 2023

Data Ingestion and Transformation: Candidates should have experience with data ingestion techniques, such as bulk and incremental loading, as well as experience with data transformation using Azure Data Factory. SQL is also an essential skill for Azure Data Engineers.

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 52

Knowledge Hut

NOVEMBER 28, 2023

SQL Today, more and more cloud-based systems add SQL-like interfaces that allow you to use SQL. ETL is central to getting your data where you need it. Relational database management systems (RDBMS) remain the key to data discovery and reporting, regardless of their location.

Data Engineering 52

Data Engineering Data Engineer Engineering Generalist 52

ProjectPro

APRIL 1, 2016

News on Hadoop- March 2016 Hortonworks makes its core more stable for Hadoop users. PCWorld.com Hortonworks is going a step further in making Hadoop more reliable when it comes to enterprise adoption. Hortonworks Data Platform 2.4, Source: [link] ) Syncsort makes Hadoop and Spark available in native Mainframe.

Hadoop 52

Hadoop BI Big Data Big Data Tools 52

ProjectPro

JANUARY 31, 2023

Big Data is a collection of large and complex semi-structured and unstructured data sets that have the potential to deliver actionable insights using traditional data management tools. Big data operations require specialized tools and techniques since a relational database cannot manage such a large amount of data.

Big Data 52

Big Data Hadoop AWS Relational Database 52

ProjectPro

OCTOBER 28, 2015

Apache Hadoop is synonymous with big data for its cost-effectiveness and its attribute of scalability for processing petabytes of data. Data analysis using hadoop is just half the battle won. Getting data into the Hadoop cluster plays a critical role in any big data deployment.

ETL Tools 52

ETL Tools Hadoop Relational Database Unstructured Data 52

Knowledge Hut

JANUARY 24, 2024

Supports numerous data sources It connects to and fetches data from a variety of data sources using Tableau and supports a wide range of data sources, including local files, spreadsheets, relational and non-relational databases, data warehouses, big data, and on-cloud data.

BI 98

BI Business Intelligence Non-relational Database Data Analysis 98

ProjectPro

SEPTEMBER 11, 2015

Hadoop has now been around for quite some time. But this question has always been present as to whether it is beneficial to learn Hadoop, the career prospects in this field and what are the pre-requisites to learn Hadoop? By 2018, the Big Data market will be about $46.34 Big Data is not going to go away.

Hadoop 52

Hadoop Java BI Big Data 52

U-Next

MARCH 7, 2023

How to track changes in databases? Change data capture, and change tracking are two features of SQL Server that track changes to data. What are the basic skills needed to become a Data Analyst? Data mining, report writing, and relational databases are also part of business intelligence, which includes OLAP.

Data Mining 96

Data Mining Scala Government Data Governance 96

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

AltexSoft

MAY 14, 2021

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. This data isn’t just about structured data that resides within relational databases as rows and columns. Data storage and processing. Apache Hadoop.

Big Data 78

Big Data Data Analytics IT NoSQL 78

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. Cassandra A database built by the Apache Foundation.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

Edureka

FEBRUARY 9, 2023

In this blog on “Azure data engineer skills”, you will discover the secrets to success in Azure data engineering with expert tips, tricks, and best practices Furthermore, a solid understanding of big data technologies such as Hadoop, Spark, and SQL Server is required.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Mining 52

ProjectPro

JULY 27, 2021

Differentiate between relational and non-relational database management systems. Relational Database Management Systems (RDBMS) Non-relational Database Management Systems Relational Databases primarily work with structured data using SQL (Structured Query Language).

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

Knowledge Hut

SEPTEMBER 26, 2023

We as Azure Data Engineers should have extensive knowledge of data modelling and ETL (extract, transform, load) procedures in addition to extensive expertise in creating and managing data pipelines, data lakes, and data warehouses. ETL activities are also the responsibility of data engineers.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

Edureka

FEBRUARY 7, 2023

Dynamic data masking serves several important functions in data security. It is possible to use Azure SQL Database, Azure SQL Managed Instance and Azure Synapse Analytics. It can be set up as a security policy on all SQL Databases in an Azure subscription.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Lake 52

Cloud Academy

JANUARY 27, 2022

Let’s review some of the big picture concepts as well finer details about being a data engineer. What does a data engineer do – the big picture Data engineers will often be dealing with raw data. They need to understand common data formats and interfaces, and the pros and cons of different storage options.

Data Engineering 52

Data Engineering Data Engineer Certification Engineering 52

ProjectPro

NOVEMBER 5, 2014

Hadoop is the way to go for organizations that do not want to add load to their primary storage system and want to write distributed jobs that perform well. MongoDB NoSQL database is used in the big data stack for storing and retrieving one item at a time from large datasets whereas Hadoop is used for processing these large data sets.

MongoDB 40

MongoDB Hadoop NoSQL Big Data 40

AltexSoft

JUNE 26, 2023

Such tables create the basis for business intelligence, traditional data analytics, and time series forecasting (if data about the same item is collected at different points of time.) Structured data is modeled to be easily searchable and occupy minimal storage space. The difference between data warehouses, lakes, and marts.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

ProjectPro

NOVEMBER 14, 2015

Big data has taken over many aspects of our lives and as it continues to grow and expand, big data is creating the need for better and faster data storage and analysis. These Apache Hadoop projects are mostly into migration, integration, scalability, data analytics, and streaming analysis. Data Migration 2.

Hadoop 52

Hadoop Project Big Data Healthcare 52

ProjectPro

OCTOBER 15, 2014

Pig and Hive are the two key components of the Hadoop ecosystem. What does pig hadoop or hive hadoop solve? Pig hadoop and Hive hadoop have a similar goal- they are tools that ease the complexity of writing complex java MapReduce programs. Apache HIVE and Apache PIG components of the Hadoop ecosystem are briefed.

Hadoop 52

Hadoop Unstructured Data Java SQL 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

Towards Data Science

MARCH 5, 2024

At its core, BigQuery is a serverless Data Warehouse for analytical purposes and built-in features like Machine Learning ( BigQuery ML ). Traditionally, normalization has been hailed as a best practice, emphasizing the reduction of redundancy and the preservation of data integrity.

Bytes 70

Bytes Google Cloud Cloud Storage Utilities 70

Knowledge Hut

SEPTEMBER 6, 2023

Data Analysis : Strong data analysis skills will help you define ways and strategies to transform data and extract useful insights from the data set. Big Data Frameworks : Familiarity with popular Big Data frameworks such as Hadoop, Apache Spark, Apache Flink, or Kafka are the tools used for data processing.

Big Data 52

Big Data Certification Hadoop Scala 52

AltexSoft

AUGUST 29, 2023

The term data lake itself is metaphorical, evoking an image of a large body of water fed by multiple streams, each bringing new data to be stored and analyzed. Instead of relying on traditional hierarchical structures and predefined schemas, as in the case of data warehouses, a data lake utilizes a flat architecture.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Grouparoo

OCTOBER 6, 2021

This provided a nice overview of the breadth of topics that are relevant to data engineering including data warehouses/lakes, pipelines, metadata, security, compliance, quality, and working with other teams. 69 The End of ETL as We Know It Use events from the product to notify data systems of changes. Be adaptable.

Data Engineering 52

Data Engineering Data Engineer Engineering Pipeline-centric 52

AltexSoft

FEBRUARY 11, 2023

It serves as a foundation for the entire data management strategy and consists of multiple components including data pipelines; , on-premises and cloud storage facilities – data lakes , data warehouses , data hubs ;, data streaming and Big Data analytics solutions ( Hadoop , Spark , Kafka , etc.);

Data Architect 87

Data Architect Certification Generalist Big Data 87

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

Knowledge Hut

MARCH 20, 2024

To work with a range of databases, you must learn SQL Scripts and be able to write SQL. To ensure that the data is reliable, consistent, and easily accessible, data engineers work with various data storage platforms, such as relational databases, NoSQL databases, and data warehouses.

Software Engineer 52

Software Engineer Software Engineering Engineering Java 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

Knowledge Hut

JANUARY 30, 2024

This data can be structured, semi-structured, or entirely unstructured, making it a versatile tool for collecting information from various origins. The extracted data is then duplicated or transferred to a designated destination, often a data warehouse optimized for Online Analytical Processing (OLAP).

ETL Tools 52

ETL Tools Database-centric Data Mining Data Cleanse 52

AltexSoft

DECEMBER 23, 2022

It is a data integration process with which you first extract raw information (in its original formats) from various sources and load it straight into a central repository such as a cloud data warehouse , a data lake , or a data lakehouse where you transform it into suitable formats for further analysis and reporting.

Process 52

Process Building Raw Data Data Lake 52

U-Next

AUGUST 10, 2022

The essential knowledge base for Data Engineers is SQL. Without a solid understanding of SQL, you cannot administer an RDBMS (relational database management). Database Management: Understanding how to create and operate a data warehouse is a crucial skill.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Science 52