Data Engineering Weekly

JUNE 10, 2024

[link] Piethein Strengholt: Integrating Azure Databricks and Microsoft Fabric Databricks buying Tabluar certainly triggers interesting patterns in the data infrastructure. Databricks and Snowflake offer a data warehouse on top of cloud providers like AWS, Google Cloud, and Azure. Will they co-exist or fight with each other?

Data Engineering 103

Data Engineering Data Engineer Engineering Kafka 103

Cloudera

NOVEMBER 1, 2022

To quote Gartner VP Sid Nag, the “irrational exuberance of procuring cloud services” gave way to a more rational approach that prioritizes governance and security over which cloud to migrate workloads to, be it public, private, or hybrid. . Learn more about CDP Private Cloud here.

Cloud 89

Cloud Government Cloud Computing Data Architecture 89

The Modern Data Company

MAY 10, 2023

In our previous post, The Pros and Cons of Leading Data Management and Storage Solutions , we untangled the differences among data lakes, data warehouses, data lakehouses, data hubs, and data operating systems. Does not have the resources to implement robust data governance and management.

Data Management 52

Data Management Management Data Lake Data Warehouse 52

The Modern Data Company

MAY 10, 2023

In our previous post, The Pros and Cons of Leading Data Management and Storage Solutions , we untangled the differences among data lakes, data warehouses, data lakehouses, data hubs, and data operating systems. Does not have the resources to implement robust data governance and management.

Data Management 52

Data Management Management Data Lake Data Warehouse 52

The Modern Data Company

MAY 10, 2023

In our previous post, The Pros and Cons of Leading Data Management and Storage Solutions , we untangled the differences among data lakes, data warehouses, data lakehouses, data hubs, and data operating systems. Does not have the resources to implement robust data governance and management.

Data Management 52

Data Management Management Data Lake Data Warehouse 52

Databand.ai

JULY 19, 2023

A Beginner’s Guide [SQ] Niv Sluzki July 19, 2023 ELT is a data processing method that involves extracting data from its source, loading it into a database or data warehouse, and then later transforming it into a format that suits business needs. The data is loaded as-is, without any transformation.

Data Cleanse 52

Data Cleanse Data Storage Raw Data 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. However, data warehouses can experience limitations and scalability challenges.

Data Management 52

Data Management Management Data Lake 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 Management Data Lake 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 Management Data Lake Data Governance 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

Cloudera

AUGUST 21, 2024

The Awards showcase IT vendor offerings that provide significant technology advances – and partner growth opportunities – across technology categories including AI and AI infrastructure, cloud management tools, IT infrastructure and monitoring, networking, data storage, and cybersecurity.

Unstructured Data 66

Unstructured Data Business Intelligence Data Architecture Data Warehouse 66

phData: Data Engineering

SEPTEMBER 19, 2023

This blog will guide you through the best data modeling methodologies and processes for your data lake, helping you make informed decisions and optimize your data management practices. What is a Data Lake? What are Data Modeling Methodologies, and Why Are They Important for a Data Lake?

Data Lake 52

Data Lake Process Metadata Data Warehouse 52

Data Engineering Weekly

OCTOBER 6, 2024

The key benefits are Improved data quality, Enhanced data governance Increased security Cost efficiency [link] HomeToGo: How HomeToGo improved our Superset Monitoring Framework Apache Superset is the most popular open-source BI tool in the industry. “text-to-SQL” and “text-to-insight.”

Data Engineering 62

Data Engineering Data Engineer Engineering Data Warehouse 62

Knowledge Hut

MARCH 28, 2024

Job Role 1: Azure Data Engineer Azure Data Engineers develop, deploy, and manage data solutions with Microsoft Azure data services. They use many data storage, computation, and analytics technologies to develop scalable and robust data pipelines. GDPR, HIPAA), and industry standards.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Warehouse 52

Data Engineering Weekly

NOVEMBER 26, 2023

We saw a fleet of announcements from Data Catalogs tools on how LLM can help to auto-generate documentation [See: How Generative AI Is Making Data Catalogs Smarter ]. I believe the impact of LLM will go further down in the stack with data storage formats in the coming years. Let me know your thoughts in the comments.

Data Engineering 70

Data Engineering Data Engineer Engineering Kafka 70

Snowflake

MARCH 30, 2023

A 2016 data science report from data enrichment platform CrowdFlower found that data scientists spend around 80% of their time in data preparation (collecting, cleaning, and organizing of data) before they can even begin to build machine learning (ML) models to deliver business value.

Engineering 105

Engineering Raw Data Data Science Machine Learning 105

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 Relational Database Data Governance 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

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

Cloudera

DECEMBER 2, 2020

With access to vast amounts of data from its customer base, the company knew its ability to mine this data would be a key driver of positive transformation. However, it was locked into an expensive legacy data warehouse which resulted in high operational costs and the inability to perform exploratory analytics.

Telecommunication 67

Telecommunication Data Science Business Intelligence Data Warehouse 67

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

Databand.ai

JULY 6, 2023

Data Integrity Testing: Goals, Process, and Best Practices Niv Sluzki July 6, 2023 What Is Data Integrity Testing? Data integrity testing refers to the process of validating the accuracy, consistency, and reliability of data stored in databases, data warehouses, or other data storage systems.

Data Integration 52

Data Integration Process Data Validation 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

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

AltexSoft

AUGUST 29, 2023

In 2010, a transformative concept took root in the realm of data storage and analytics — a data lake. The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 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

ProjectPro

JANUARY 31, 2022

As the demand for big data grows, an increasing number of businesses are turning to cloud data warehouses. The cloud is the only platform to handle today's colossal data volumes because of its flexibility and scalability. Launched in 2014, Snowflake is one of the most popular cloud data solutions on the market.

Architecture 52

Architecture IT Data Warehouse Amazon Web Services 52

Monte Carlo

APRIL 24, 2023

Data lakes are useful, flexible data storage repositories that enable many types of data to be stored in its rawest state. Traditionally, after being stored in a data lake, raw data was then often moved to various destinations like a data warehouse for further processing, analysis, and consumption.

Data Lake 52

Data Lake Google Cloud Data Warehouse AWS 52

ProjectPro

FEBRUARY 8, 2023

ETL stands for Extract, Transform, and Load, which involves extracting data from various sources, transforming the data into a format suitable for analysis, and loading the data into a destination system such as a data warehouse. Data Governance Know-how of data security, compliance, and privacy.

ETL Tools 52

ETL Tools Data Cleanse Data Warehouse Big Data 52

Edureka

FEBRUARY 27, 2023

Here are some of the common types: Data Warehouses: A data warehouse is a centralized repository of information that can be used for reporting and analysis. Data warehouses typically contain historical data that can be used to track trends over time.

Business Intelligence 52

Business Intelligence BI Data Mining Data Warehouse 52

Monte Carlo

MAY 14, 2024

It bridges the gap between traditional databases and the big data world, providing a platform for complex data transformations and batch processing in environments that require deep data analysis. And that matters — because these new table formats are also introducing complexity in other ways.

Data Lake 52

Data Lake Metadata Hadoop Data Governance 52

Knowledge Hut

APRIL 25, 2023

Data engineers add meaning to the data for companies, be it by designing infrastructure or developing algorithms. The practice requires them to use a mix of various programming languages, data warehouses, and tools. While they go about it - enter big data data engineer tools.

Data Engineering 52

Data Engineering Data Engineer Engineering Google Cloud 52

phData: Data Engineering

MARCH 11, 2024

For years, marketing teams across industries have turned to implementing traditional Customer Data Platforms (CDPs) as separate systems purpose-built to unlock growth with first-party data. The ELT platform offers 200+ pre-built connections to centralize data to any data platform. dbt has become the standard for modeling.

Data Warehouse 52

Data Warehouse Data Data Storage Cloud 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

Knowledge Hut

JULY 5, 2024

Data engineers like myself play a pivotal role in assessing infrastructure and taking relevant actions. Looking ahead, the future of data engineering appears promising. With the increasing computing power of various cloud data warehouses, data engineers will be capable of efficiently handling large-scale tasks.

Data Engineering 52

Data Engineering Data Engineer Engineering Hadoop 52

ProjectPro

FEBRUARY 8, 2017

The need for speed to use Hadoop for sentiment analysis and machine learning has fuelled the growth of hadoop based data stores like Kudu and adoption of faster databases like MemSQL and Exasol. 2) Big Data is no longer just Hadoop A common misconception is that Big Data and Hadoop are synonymous.

Big Data 40

Big Data Hadoop Data Lake Machine Learning 40

phData: Data Engineering

JULY 18, 2023

With the birth of cloud data warehouses, data applications, and generative AI , processing large volumes of data faster and cheaper is more approachable and desired than ever. First up, let’s dive into the foundation of every Modern Data Stack, a cloud-based data warehouse.

Data Warehouse 52

Data Warehouse Pipeline-centric Government Data 52