Data Engineering Weekly

OCTOBER 30, 2022

link] Sarah Krasnik: The Analytics Requirements Document The first critical step to bringing data-driven culture into an organization is to embed the data collection and analytical requirement part of the product development workflow.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Ingestion 52

Cloudera

SEPTEMBER 28, 2020

This customer’s workloads leverage batch processing of data from 100+ backend database sources like Oracle, SQL Server, and traditional Mainframes using Syncsort. Data Science and machine learning workloads using CDSW. The customer is a heavy user of Kafka for data ingestion. using the steps documented here.

Cloud 131

Cloud Kafka Professional Services Metadata 131

Cloudera

AUGUST 31, 2021

Running on CDW is fully integrated with streaming, data engineering, and machine learning analytics. It has a consistent framework that secures and provides governance for all data and metadata on private clouds, multiple public clouds, or hybrid clouds. Consideration of both data & metadata in the migration.

Data Warehouse 82

Data Warehouse Database-centric Metadata Cloud 82

Maxime Beauchemin

JANUARY 20, 2017

This opens new moves in the data modeler’s playbook, and can allow for fact tables to store multiple grains at once when needed dynamic schemas : since the advent of map reduce, with the growing popularity of document stores and with support for blobs in databases, it’s becoming easier to evolve database schemas without executing DML.

Data Engineering 130

Data Engineering Data Engineer Engineering ETL Tools 130

dbt Developer Hub

JANUARY 14, 2023

The builder: I’m a data freelancer who deploys end to end solutions, so when I have a data problem, I cannot just let it go. dlt is a new Python library for declarative data ingestion which I have wanted to test for some time. Finally, I will use dbt Core for transformation. file for the source).

Data Ingestion 52

Data Ingestion Google Cloud Python Cloud 52

Knowledge Hut

SEPTEMBER 29, 2023

Microsoft learning platform: Azure data engineering training is officially documented by Microsoft. These documents are mostly text-based, with videos used to help self-learners understand some of the ideas. To understand the capabilities in detail, you must see the official documentation.

Certification 52

Certification Data Engineering Data Engineer Engineering 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

Monte Carlo

JULY 10, 2023

Data sampling If you’re working with large data sets where it’s impractical to evaluate every attribute or record, you can systematically sample your data set to estimate completeness. Be sure to use random sampling to select representative subsets of your data.

Data Collection 40

Data Collection Data Governance Government Data 40

Confluent

FEBRUARY 21, 2019

During a recent talk titled Hunters ATT&CKing with the Right Data , which I presented with my brother Jose Luis Rodriguez at ATT&CKcon, we talked about the importance of documenting and modeling security event logs before developing any data analytics while preparing for a threat hunting engagement.

Process 80

Process Kafka Datasets SQL 80

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

Monte Carlo

APRIL 4, 2023

That’s why, in addition to integrating with your central data warehouse , lake , and lakehouse , Monte Carlo also integrates with transformation , orchestration , and now data ingestion tools. Now teams can instantly get full visibility into how these systems may be impacting their data assets, all in a single pane of glass.

BI 64

BI Data Ingestion Metadata Data Pipeline 64

Cloudera

MARCH 31, 2021

Customers who have chosen Google Cloud as their cloud platform can now use CDP Public Cloud to create secure governed data lakes in their own cloud accounts and deliver security, compliance and metadata management across multiple compute clusters. Data Preparation (Apache Spark and Apache Hive) .

Google Cloud 91

Google Cloud Cloud Amazon Web Services Cloud Storage 91

Netflix Tech

DECEMBER 14, 2018

In the previous blog posts in this series, we introduced the N etflix M edia D ata B ase ( NMDB ) and its salient “Media Document” data model. 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.

Media 94

Media Database Metadata Data Schemas 94

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. Data integration , on the other hand, happens later in the data management flow.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

AltexSoft

SEPTEMBER 21, 2023

Data in Elasticsearch is organized into documents, which are then categorized into indices for better search efficiency. Each document is a collection of fields, the basic data units to be searched. Fields in these documents are defined and governed by mappings akin to a schema in a relational database.

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

NOVEMBER 28, 2022

CDC data often comes in deeply nested objects with complex schemas and lots of data that isn’t required by the destination. With an ingest transformation, you can easily restructure the incoming documents, clean up names, and map source fields to Rockset’s special fields.

Data Warehouse 52

Data Warehouse PostgreSQL MongoDB Data Pipeline 52

Towards Data Science

MARCH 5, 2024

This can be very beneficial for data structures that are directly used by end users via third party tools like PowerBI or Excel to further increase speed and reduce costs for certain filter operations. More information about this can be found in the official search index documentation. Also this query comes at 0 costs.

Bytes 72

Bytes Google Cloud Cloud Storage Utilities 72

Cloudera

APRIL 22, 2021

Collects and aggregates metadata from components and present cluster state. Metadata in cluster is disjoint across components. This architecture allows for: Extremely fast data ingest, and data engineering done at the data lake. You can find the Cisco Validated Design document published here.

Pipeline-centric 99

Pipeline-centric Data Lake Hadoop Metadata 99

Monte Carlo

JUNE 14, 2023

Why is data pipeline architecture important? Databricks – Databricks, the Apache Spark-as-a-service platform, has pioneered the data lakehouse, giving users the options to leverage both structured and unstructured data and offers the low-cost storage features of a data lake.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

ProjectPro

NOVEMBER 15, 2021

Apache Zeppelin Source: Github Apache Zeppelin is a multi-purpose notebook that supports Data Ingestion, Data Discovery, Data Analytics , Data Visualization , and Data Collaboration. For documentation and contribution insights: [link] 17. Vespa is a low-latency computing engine for massive data sets.

Big Data 52

Big Data Project Metadata Programming Language 52

Cloudera

JUNE 4, 2018

With over 1 PB of research data stored in a variety of systems (Documentum, Window and Unix file shares, and SharePoint) and a wide range of content types (internal reports, emails, research documents, electronic lab notes, drug profiles, clinical trials, regulatory reports, images, etc.),

Pharmaceutical 46

Pharmaceutical Unstructured Data Electronics Metadata 46

ProjectPro

AUGUST 24, 2021

Data Engineering Project for Beginners If you are a newbie in data engineering and are interested in exploring real-world data engineering projects, check out the list of data engineering project examples below. This big data project discusses IoT architecture with a sample use case.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

Ascend.io

MARCH 14, 2023

There are different ways you can make data domain products discoverable and sharable. A spreadsheet might be enough for smaller domains, while more complex domains will likely publish their metadata, owners, origins, sample datasets, and schema to a central repository or catalog. appeared first on Ascend.io.

Government 52

Government Architecture Data Lake Data 52

Ascend.io

MARCH 14, 2023

There are different ways you can make data domain products discoverable and sharable. A spreadsheet might be enough for smaller domains, while more complex domains will likely publish their metadata, owners, origins, sample datasets, and schema to a central repository or catalog. appeared first on Ascend.io.

Government 52

Government Architecture Data Lake Data 52

AltexSoft

JULY 29, 2022

A master node called NameNode maintains metadata with critical information, controls user access to the data blocks, makes decisions on replications, and manages slaves. Instruments like Apache ZooKeeper and Apache Oozie help better coordinate operations, schedule jobs, and track metadata across a Hadoop cluster. Let’s see why.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

Ascend.io

MAY 19, 2023

Stage 3 begins as these early adopters collaborate formally and informally, identifying and documenting best practices and patterns in the form of “reference architectures”. In our case, data ingestion, transformation, orchestration, reverse ETL, and observability. This is the modern data stack as we know it today.

Data Pipeline 52

Data Pipeline Data Engineering Data Engineer Media 52

Ascend.io

MARCH 17, 2023

Activities like data entry, document management, and payroll were time-consuming and prone to mistakes. For data pipeline automation, these previous approaches have foundered on one important barrier: the need to scale. Scaling these workflows meant hiring lots of people to do the same thing. appeared first on Ascend.io.

Data Pipeline 52

Data Pipeline Datasets Data Software Engineer 52

Ascend.io

MARCH 17, 2023

Activities like data entry, document management, and payroll were time-consuming and prone to mistakes. For data pipeline automation, these previous approaches have foundered on one important barrier: the need to scale. Scaling these workflows meant hiring lots of people to do the same thing. appeared first on Ascend.io.

Data Pipeline 52

Data Pipeline Datasets Data Software Engineer 52

ProjectPro

OCTOBER 20, 2021

Make sure that the quality of data works for your use case. Data Ingestion Exploratory Data Analysis (using RAD Tools) Validation Data Wrangling Data Splitting Model: Machine Learning Pipelines Every team enjoys experimentation with data. 29) What is the difference between MLOps and DevOps?

Machine Learning 52

Machine Learning Algorithm Government Data Science 52

ProjectPro

AUGUST 21, 2015

What are the steps involved in deploying a big data solution? This data needs to be stored in HDFS. JSON stores both data and schema together in a record and also enables complete schema evolution and splitability. Avro files store metadata with data and also let you specify independent schema for reading the files.

Hadoop 40

Hadoop Big Data Metadata Java 40

AltexSoft

MAY 12, 2023

Unlike structured data, which is organized into neat rows and columns within a database, unstructured data is an unsorted and vast information collection. It can come in different forms, such as text documents, emails, images, videos, social media posts, sensor data, etc. Some examples of unstructured data Text documents.

Unstructured Data 59

Unstructured Data NoSQL Hadoop Data Lake 59

Monte Carlo

MARCH 28, 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)?

Data Warehouse 126

Data Warehouse Raw Data Data Pipeline Software Engineer 126

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)?

Data Engineering 98

Data Engineering Data Engineer Engineering Data Warehouse 98

phData: Data Engineering

JULY 18, 2023

Enterprises can effortlessly prepare data and construct ML models without the burden of complex integrations while maintaining the highest level of security. Generally, organizations need to integrate a wide variety of source systems when building their analytics platform, each with its own specific data extraction requirements.

Data Warehouse 52

Data Warehouse Pipeline-centric Government Data 52

AltexSoft

MARCH 30, 2023

Databricks architecture Databricks provides an ecosystem of tools and services covering the entire analytics process — from data ingestion to training and deploying machine learning models. Besides that, it’s fully compatible with various data ingestion and ETL tools. Let’s see what exactly Databricks has to offer.

Scala 64

Scala Data Lake BI Google Cloud 64

Monte Carlo

AUGUST 19, 2023

We’ll cover: What is a data platform? Databricks – Databricks, the Apache Spark-as-a-service platform, has pioneered the data lakehouse, giving users the options to leverage both structured and unstructured data and offers the low-cost storage features of a data lake.

Building 52

Building BI Data Lake Data Governance 52