Data Engineering Weekly

MARCH 3, 2024

The migration enhanced data quality, lineage visibility, performance improvements, cost reductions, and better reliability and scalability, setting a robust foundation for future expansions and onboarding. This approach helps maintain accuracy, relevance, and compliance in generative AI applications.

Data Engineering 67

Data Engineering Data Engineer Pipeline-centric Engineering 67

Christophe Blefari

JUNE 16, 2023

The power of pre-commit and SQLFluff —SQL is a query programming language used to retrieve information from data storages, and like any other programming language, you need to enforce checks at all times. This is where you should use pre-commit and SQLFluff. PS: I just split the Fast News to have a smaller one.

Programming Language 130

Programming Language SQL PostgreSQL Data 130

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? They provide a framework for organizing and representing data elements, attributes, and relationships.

Data Lake 52

Data Lake Process Metadata Data Warehouse 52

Cloudera

JUNE 27, 2023

An MDA allows you to identify silos and disparate processes, providing visibility across data functions and assets allowing rapid consolidation and harmonization. When you deploy a platform that supports MDA you can consolidate other systems, like legacy data mediation and disparate data storage solutions.

Data Architecture 61

Data Architecture Architecture Data Governance Government 61

Databand.ai

JUNE 21, 2023

Data integrity tools are software applications or systems designed to ensure the accuracy, consistency, and reliability of data stored in databases, spreadsheets, or other data storage systems. By doing so, data integrity tools enable organizations to make better decisions based on accurate, trustworthy information.

Data Integration 52

Data Integration Data Cleanse Data Validation Telecommunication 52

Knowledge Hut

APRIL 23, 2024

Small Data is well-suited for focused decision-making, where specific insights drive actions. Big Data vs Small Data: Storage and Cost Big Data: Managing and storing Big Data requires specialized storage systems capable of handling large volumes of data.

Big Data 52

Big Data Datasets Data Analysis Media 52

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 84

Cloud Government Cloud Computing Data Architecture 84

Edureka

FEBRUARY 7, 2023

An Azure Data Engineer is responsible for designing, implementing, and maintaining data management and data processing systems on the Microsoft Azure cloud platform. They work with large and complex data sets and are responsible for ensuring that data is stored, processed, and secured efficiently and effectively.

Data Engineering 52

Data Engineering Data Engineer Engineering Programming Language 52

Knowledge Hut

APRIL 23, 2024

Complexity Complexity is governed not just by data size but also by variety, type, speed, processing challenges, and retrieval methods. The more complex your data, the higher the chances of data processing inefficiency.

Big Data 52

Big Data Media Government Big Data Skills 52

Precisely

MARCH 23, 2023

The Data Integrity Capabilities You Need for ESG Reporting Now that we’ve covered why you need to have clear, accurate, and readily accessible data to show the progress of your ESG efforts, let’s walk through a snapshot of what that means for your data management capabilities.

Data Integration 52

Data Integration Government High Quality Data Data 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

ProjectPro

FEBRUARY 8, 2023

The role of an ETL developer is to extract data from multiple sources, transform it into a usable format and load it into a data warehouse or any other destination database. ETL developers are the backbone of a successful data management strategy as they ensure that the data is consistent and accurate for data-driven decision-making.

ETL Tools 52

ETL Tools Data Cleanse Data Warehouse Big Data 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. Notice how Snowflake dutifully avoids (what may be a false) dichotomy by simply calling themselves a “data cloud.” AWS is one of the most popular data lake vendors.

Data Lake 52

Data Lake Google Cloud Data Warehouse AWS 52

Knowledge Hut

JULY 26, 2023

These datasets typically involve high volume, velocity, variety, and veracity, which are often referred to as the 4 v's of Big Data: Volume: Volume refers to the vast amount of data generated and collected from various sources. Managing and analyzing such large volumes of data requires specialized tools and technologies.

Big Data 52

Big Data Data Cleanse Retail Healthcare 52

Ascend.io

DECEMBER 19, 2023

While Snowflake offers robust features that are particularly valuable for certain aspects of data handling, there are several reasons why a separate ETL tool might still be necessary or advantageous: Chaining Together Complex Data Transformations: Snowflake is highly efficient at handling data storage and basic to moderately complex transformations.

ETL Tools 52

ETL Tools IT Data Pipeline Data Warehouse 52

AltexSoft

MARCH 2, 2023

As the amount of enterprise data continues to surge, businesses are increasingly recognizing the importance of data governance — the framework for managing an organization’s data assets for accuracy, consistency, security, and effective use. What is data governance? billion in 2020 to $5.28

Data Governance 52

Data Governance Government Healthcare Programming 52

Striim

NOVEMBER 8, 2023

Data consistency is ensured through uniform definitions and governance requirements across the organization, and a comprehensive communication layer allows other teams to discover the data they need. As a result, implementing data governance in this centralized system can be burdensome.

Architecture 52

Architecture Generalist Government Datasets 52

Cloudera

MAY 9, 2019

It is meant for you to assess if you have thought through processes such as continuous data ingestion, enterprise data integration and data governance. Data infrastructure readiness – IoT architectures can be insanely complex and sophisticated. Will you be needing local edge storage?

Manufacturing 53

Manufacturing Data Ingestion Architecture Data Governance 53

Knowledge Hut

JUNE 20, 2023

Cloud Computing, being a centralized form of data storage and processing network, helps companies carry out such activities very conveniently throughout the organization. Organizations can store and analyze data on remote servers using cloud-based analytics.

Business Analyst 52

Business Analyst Banking Unstructured Data Cloud Computing 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. Potential downsides of data lakes include governance and integration 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. Potential downsides of data lakes include governance and integration 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. Potential downsides of data lakes include governance and integration challenges.

Data Management 52

Data Management Data Lake Management Data Governance 52

ProjectPro

JULY 1, 2016

Insidebigdata.com The leading provider of big data management software solutions Attunity released its latest software solution Attunity Visibility for Hadoop. Source: [link] ) PREVIOUS NEXT <

Hadoop 40

Hadoop Big Data Data Lake Datasets 40

Databand.ai

JULY 19, 2023

The ELT process relies heavily on the power and scalability of modern data storage systems. By loading the data before transforming it, ELT takes full advantage of the computational power of these systems. This approach allows for faster data processing and more flexible data management compared to traditional methods.

Data Cleanse 52

Data Cleanse Raw Data Data Storage 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.

Data Lake 52

Data Lake Metadata Hadoop Data Governance 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. For instance, in the late 2010s, Uber changed all data analysts’ titles to data scientists after an organizational restructure.

Data Lake 52

Data Lake Data Pipeline Unstructured Data Data Warehouse 52

Knowledge Hut

DECEMBER 22, 2023

Hadoop Gigabytes to petabytes of data may be stored and processed effectively using the open-source framework known as Apache Hadoop. Hadoop enables the clustering of many computers to examine big datasets in parallel more quickly than a single powerful machine for data storage and processing.

Hadoop 98

Hadoop Deep Learning Data Science Machine Learning 98

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

Snowflake

NOVEMBER 29, 2023

While the former can be solved by tokenization strategies provided by external vendors, the latter mandates the need for patient-level data enrichment to be performed with sufficient guardrails to protect patient privacy, with an emphasis on auditability and lineage tracking. The principles emphasize machine-actionability (i.e.,

Metadata 83

Metadata Healthcare Medical Data Storage 83

ProjectPro

FEBRUARY 21, 2023

Due to the enormous amount of data being generated and used in recent years, there is a high demand for data professionals, such as data engineers, who can perform tasks such as data management, data analysis, data preparation, etc. big data and ETL tools, etc.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

Knowledge Hut

SEPTEMBER 6, 2023

These certifications serve as concrete evidence of a professional's mastery, encompassing skills such as precise querying, data manipulation, database creation, and various data-related tasks. Skills acquired : Core data concepts. Concept of structured, semi-structured, and unstructured data. Data storage options.

Database 52

Database Certification MongoDB MySQL 52

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

FEBRUARY 1, 2024

How brand advertisers and media and entertainment companies use consumer data — and to what degree an individual consumer is identifiable with that data — has led to a global network of regulatory requirements that require strict data governance, expertise and resources.

Entertainment 87

Entertainment Media Data Governance Government 87

phData: Data Engineering

AUGUST 4, 2023

Data storage is a vital aspect of any Snowflake Data Cloud database. Within Snowflake, data can either be stored locally or accessed from other cloud storage systems. Snowflake stores and manages data in the cloud using a shared disk approach, which simplifies data management.

Cloud Storage 52

Cloud Storage Google Cloud Amazon Web Services Data Storage 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

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

Data Engineering Weekly

NOVEMBER 13, 2022

link] Meta: Tulip - Schematizing Meta’s data platform Numerous heterogeneous services make up a data platform, such as warehouse data storage and various real-time systems. The schematization of data plays a vital role in a data platform. The author shares the experience of one such transition.

Data Engineering 52

Data Engineering Data Engineer Engineering Kafka 52