Data Engineering Weekly

OCTOBER 30, 2022

Before you add another tool to your data stack, check out our latest guide for 10 things to consider when evaluating data observability platforms, including scalability, time to value, and ease of setup. The modern data stack is trying to address this problem in a silo; the org eventually has to tie everything to make it work.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Ingestion 52

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. This stage handles all the aspects of data storage like organization, file size, structure, compression, metadata, statistics.

Cloud Storage 52

Cloud Storage Data Ingestion Data Cleanse Data Warehouse 52

Ascend.io

JUNE 8, 2023

Acting as the core infrastructure, data pipelines include the crucial steps of data ingestion, transformation, and sharing. Data Ingestion Data in today’s businesses come from an array of sources, including various clouds, APIs, warehouses, and applications.

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 52

Monte Carlo

OCTOBER 5, 2023

Data freshness (aka data timeliness) means your data should be up-to-date and relevant to the timeframe of analysis. Data validity means your data conforms to the required format, type, or range of values. Example: Email addresses in the customer database should match a valid format (e.g.,

Government 52

Government Data Governance Data Datasets 52

DataKitchen

SEPTEMBER 21, 2023

In the contemporary data landscape, data teams commonly utilize data warehouses or lakes to arrange their data into L1, L2, and L3 layers. These layers help teams delineate different stages of data processing, storage, and access, offering a structured approach to data management.

Raw Data 67

Raw Data Data Business Intelligence High Quality Data 67

Knowledge Hut

SEPTEMBER 25, 2023

Data Engineer Design, implement, and maintain data pipelines for data ingestion, processing, and transformation in Azure. Work together with data scientists and analysts to understand the needs for data and create effective data workflows.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Lake 52

Monte Carlo

JULY 10, 2023

Accuracy reflects the degree to which the data correctly describes the “real-world” objects being described. For example, let’s say a streaming provider has 10 million overall subscribers who can access its content. According to the CRM’s data set, the streaming provider has 13 million subscribers.

Data Collection 40

Data Collection Data Governance Government Data 40

Cloudera

AUGUST 31, 2021

These schemas will be created based on its definitions in existing legacy data warehouses. Smart DwH Mover helps in accelerating data warehouse migration. Smart Data Validator helps in extensive data reconciliation and testing. Mainframe CDC using IBM Infosphere Data Replicator (IIDR). Value Achieved.

Data Warehouse 82

Data Warehouse Database-centric Metadata Cloud 82

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. Enriching data entails connecting it to other related data to produce deeper insights.

Big Data 52

Big Data Hadoop AWS Relational Database 52

phData: Data Engineering

JANUARY 3, 2022

The other major advantage is offline access. With SVN, you wouldn’t be able to commit any changes or create any branches without internet access. This reduces the download size of your resources and increases performance for situations where your code needs to quickly be accessible by a third party.

IT 52

IT AWS Software Engineer Software Engineering 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