Data Engineering Weekly

MARCH 24, 2024

The APIs support emitting unstructured log lines and typed metadata key-value pairs (per line). Ingestion clusters read objects from queues and support additional parsing based on user-defined regex extraction rules. The extracted key-value pairs are written to the line’s metadata.

Data Engineer 62

Data Engineer Data Engineering Engineering Metadata 62

ProjectPro

FEBRUARY 8, 2023

When Glue receives a trigger, it collects the data, transforms it using code that Glue generates automatically, and then loads it into Amazon S3 or Amazon Redshift. Then, Glue writes the job's metadata into the embedded AWS Glue Data Catalog. being data exactly matches the classifier, and 0.0 Why Use AWS Glue?

AWS 98

AWS Scala Metadata Data Lake 98

Cloudera

AUGUST 31, 2021

While this “data tsunami” may pose a new set of challenges, it also opens up opportunities for a wide variety of high value business intelligence (BI) and other analytics use cases that most companies are eager to deploy. . Traditional data warehouse vendors may have maturity in data storage, modeling, and high-performance analysis.

Data Warehouse 82

Data Warehouse Database-centric Metadata Cloud 82

Databand.ai

AUGUST 30, 2023

DataOps is a collaborative approach to data management that combines the agility of DevOps with the power of data analytics. It aims to streamline data ingestion, processing, and analytics by automating and integrating various data workflows. As a result, they can be slow, inefficient, and prone to errors.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 52

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

Netflix Tech

DECEMBER 14, 2018

A fundamental requirement for any lasting data system is that it should scale along with the growth of the business applications it wishes to serve. NMDB is built to be a highly scalable, multi-tenant, media metadata system that can serve a high volume of write/read throughput as well as support near real-time queries.

Media 97

Media Database Metadata Data Schemas 97

RandomTrees

SEPTEMBER 6, 2020

The architecture is three layered: Database Storage: Snowflake has a mechanism to reorganize the data into its internal optimized, compressed and columnar format and stores this optimized data in cloud storage. The data objects are accessible only through SQL query operations run using Snowflake.

Cloud Storage 52

Cloud Storage Data Ingestion Data Cleanse Data Warehouse 52

Databand.ai

JUNE 28, 2023

Data observability works with your data pipeline by providing insights into how your data flows and is processed from start to end. Here is a more detailed explanation of how data observability works within the data pipeline: Data ingestion : Observability begins from the point where data is ingested into the pipeline.

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Data Pipeline Data Engineer Data Engineering Engineering 98

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

Monte Carlo

JANUARY 5, 2024

This architecture format consists of several key layers that are essential to helping an organization run fast analytics on structured and unstructured data. Table of Contents What is data lakehouse architecture? The 5 key layers of data lakehouse architecture 1. Ingestion layer 2. Storage layer 3. API layer 5.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

Monte Carlo

JANUARY 5, 2024

This architecture format consists of several key layers that are essential to helping an organization run fast analytics on structured and unstructured data. Table of Contents What is data lakehouse architecture? The 5 key layers of data lakehouse architecture 1. Ingestion layer 2. Storage layer 3. API layer 5.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

Towards Data Science

APRIL 3, 2023

And so it almost seems unfair that new ideas are already springing up to disrupt the disruptors: Zero-ETL has data ingestion in its sights AI and Large Language Models could transform transformation Data product containers are eyeing the table’s thrown as the core building block of data Are we going to have to rebuild everything (again)?

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Data Engineer Data Engineering Engineering Data Warehouse 98

LinkedIn Engineering

JULY 27, 2023

The landing page lists all the resource recommendations along with metadata around resource owners (Azure security groups), recommendation message, current lifecycle status of the recommendation, due date, assigned engineer, last action message in terms of comments, and a history modal option to check the timeline of actions taken.

Metadata 75

Metadata Utilities Cloud Database 75

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 25, 2022

Data Ingestion Data Processing Data Splitting Model Training Model Evaluation Model Deployment Monitoring Model Performance Machine Learning Pipeline Tools Machine Learning Pipeline Deployment on Different Platforms FAQs What tools exist for managing data science and machine learning pipelines?

Machine Learning 52

Machine Learning Building Amazon Web Services AWS 52

AltexSoft

JULY 29, 2022

No matter the actual size, each cluster accommodates three functional layers — Hadoop distributed file systems for data storage, Hadoop MapReduce for processing, and Hadoop Yarn for resource management. You can change this parameter manually but the system won’t be able to effectively deal with myriads of tiny data pieces.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

ProjectPro

JANUARY 31, 2023

There are three steps involved in the deployment of a big data model: Data Ingestion: This is the first step in deploying a big data model - Data ingestion, i.e., extracting data from multiple data sources. Data Variety Hadoop stores structured, semi-structured and unstructured data.

Big Data 52

Big Data Hadoop Relational Database AWS 52

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. To contribute to this project, hop onto: [link] 19.DataHub

Big Data 52

Big Data Project Metadata Programming Language 52

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. MySQL An open-source relational databse management system with a client-server model.

Data Engineer 40

Data Engineer Data Engineering Engineering Hadoop 40

Monte Carlo

AUGUST 19, 2023

We’ll cover: What is a data platform? Below, we share what the “basic” data platform looks like and list some hot tools in each space (you’re likely using several of them): The modern data platform is composed of five critical foundation layers. Data Storage and Processing The first layer?

Building 52

Building BI Data Lake Data Governance 52

Snowflake

MARCH 30, 2023

3EJHjvm Once a business need is defined and a minimal viable product ( MVP ) is scoped, the data management phase begins with: Data ingestion: Data is acquired, cleansed, and curated before it is transformed. Feature engineering: Data is transformed to support ML model training. ML workflow, ubr.to/3EJHjvm

Engineering 104

Engineering Raw Data Data Science Machine Learning 104

AltexSoft

MAY 12, 2023

Tools and platforms for unstructured data management Unstructured data collection Unstructured data collection presents unique challenges due to the information’s sheer volume, variety, and complexity. The process requires extracting data from diverse sources, typically via APIs. Data durability and availability.

Unstructured Data 59

Unstructured Data NoSQL Hadoop Data Lake 59

Cloudera

JANUARY 22, 2019

Forrester describes Big Data Fabric as, “A unified, trusted, and comprehensive view of business data produced by orchestrating data sources automatically, intelligently, and securely, then preparing and processing them in big data platforms such as Hadoop and Apache Spark, data lakes, in-memory, and NoSQL.”.

Big Data 44

Big Data NoSQL Hadoop Data Lake 44

Sync Computing

NOVEMBER 7, 2024

A brief history of data storage The value of data has been apparent for as long as people have been writing things down. The data lakehouse concept shares the goals of hybrid architectures, but is designed from the ground up to meet modern needs.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Unstructured Data 52

Monte Carlo

JUNE 14, 2023

Why is data pipeline architecture important? This is frequently referred to as a 5 or 7 layer (depending on who you ask) data stack like in the image below. Here are some of the most common solutions that are involved in modern data pipelines and the role they play.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

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 hides user data objects and makes them accessible only through SQL queries through the compute layer.

Cloud Storage 52

Cloud Storage Google Cloud Amazon Web Services Data Storage 52

AltexSoft

SEPTEMBER 21, 2023

In the hospitality industry context, a single document could represent one hotel room’s data, including attributes like room number, type, price, amenities, and availability status. Each document has unique metadata fields like index , type , and id that help identify its storage location and nature.

Engineering 52

Engineering NoSQL Programming Language Java 52

Snowflake

MAY 22, 2023

Cross-Cloud Snowgrid Account Replication expands replication beyond databases – general availability Account Replication, now generally available, expands replication beyond databases to account metadata and integrations, making business continuity truly turnkey. Visit our documentation page to learn more.

Healthcare 52

Healthcare Scala Medical Transportation 52

Rockset

JUNE 6, 2023

A Primer on Rockset's Cloud-Native Architecture Rockset separates compute from storage. Virtual instances (VIs) are allocations of compute and memory resources responsible for data ingestion, transformations, and queries. These metadata files are a fixed number per database instance and they are small in size.

Metadata 52

Metadata Datasets Architecture Algorithm 52

AltexSoft

JUNE 26, 2023

Read our article on Hotel Data Management to have a full picture of what information can be collected to boost revenue and customer satisfaction in hospitality. While all three are about data acquisition, they have distinct differences. Find sources of relevant data. Choose data collection methods and tools.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

Knowledge Hut

SEPTEMBER 29, 2023

The latest Azure exam from Microsoft is structured as follows: Design and implement data storage: Creating and implementing a storage structure, a partition, and a serving layer are tested in this portion (40–45%). You can browse the data lake files with the interactive training material.

Certification 52

Certification Data Engineer Data Engineering Engineering 52

AltexSoft

MARCH 14, 2023

Batch jobs are often scheduled to load data into the warehouse, while real-time data processing can be achieved using solutions like Apache Kafka and Snowpipe by Snowflake to stream data directly into the cloud warehouse. But this distinction has been blurred with the era of cloud data warehouses.

IT 59

IT Data Warehouse Data Governance Data Lake 59

ProjectPro

NOVEMBER 22, 2021

StructType is a collection of StructField objects that determines column name, column data type, field nullability, and metadata. To define the columns, PySpark offers the pyspark.sql.types import StructField class, which has the column name (String), column type (DataType), nullable column (Boolean), and metadata (MetaData).

Hadoop 52

Hadoop Python Datasets Metadata 52

ProjectPro

AUGUST 21, 2015

Core components of a Hadoop application are- 1) Hadoop Common 2) HDFS 3) Hadoop MapReduce 4) YARN Data Access Components are - Pig and Hive Data Storage Component is - HBase Data Integration Components are - Apache Flume, Sqoop, Chukwa Data Management and Monitoring Components are - Ambari, Oozie and Zookeeper.

Hadoop 40

Hadoop Big Data Metadata Java 40

phData: Data Engineering

JULY 18, 2023

Zero Copy Cloning: Create multiple ‘copies’ of tables, schemas, or databases without actually copying the data. This noticeably saves time on copying and drastically reduces data storage costs. Data Source Tool: A multipurpose tool that collects, compares, analyzes, and acts on data source metadata and profile metrics.

Data Warehouse 52

Data Warehouse Pipeline-centric Government Data 52