AltexSoft

FEBRUARY 11, 2023

Data architecture is the organization and design of how data is collected, transformed, integrated, stored, and used by a company. Metadata management skills Metadata management unlocks the value of a company’s data and it’s a data architect’s task to ensure metadata principles are applicable to all data a business has.

Data Architect 87

Data Architect Certification Generalist Big Data 87

Monte Carlo

APRIL 24, 2023

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. Databricks Data Catalog and AWS Lake Formation are examples in this vein. AWS is one of the most popular data lake vendors.

Data Lake 52

Data Lake Google Cloud Data Warehouse AWS 52

Monte Carlo

AUGUST 25, 2023

One advantage of data warehouses is their integrated nature. As fully managed solutions, data warehouses are designed to offer ease of construction and operation. A warehouse can be a one-stop solution, where metadata, storage, and compute components come from the same place and are under the orchestration of a single vendor.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

Precisely

FEBRUARY 9, 2024

To make data AI-ready and maximize the potential of AI-based solutions, organizations will need to focus in the following areas in 2024: Access to all relevant data: When data is siloed, as data on mainframes or other core business platforms can often be, AI results are at risk of bias and hallucination.

Data Integration 69

Data Integration Government Data Metadata 69

Edureka

OCTOBER 10, 2024

NoSQL Stores: As source systems, Cassandra and MongoDB (including MongoDB Atlas), NoSQL databases are supported to make the integration of the unstructured data easy. File Systems: Data from several file systems, including FTP, SFTP, HDFS, and different cloud storages such as Amazon S3, Google cloud storage, etc.,

MongoDB 40

MongoDB NoSQL Metadata Datasets 40

Precisely

OCTOBER 10, 2024

Organizations are evaluating modern data management architectures that will support wider data democratization. Why data democratization matters First and foremost, data democratization is about empowering employees to access the data that informs better business decisions. Read Data democracy: Why now?

Data Governance 52

Data Governance Government Data Unstructured Data 52

Tweag

APRIL 19, 2023

Thankfully, cloud-based infrastructure is now an established solution which can help do this in a cost-effective way. As a simple solution, files can be stored on cloud storage services, such as Azure Blob Storage or AWS S3, which can scale more easily than on-premises infrastructure. But as it turns out, we can’t use it.

Medical 62

Medical Process Cloud Bytes 62

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

Monte Carlo

AUGUST 19, 2023

Recently, there’s been a lot of discussion around whether to go with open source or closed source solutions (the dialogue between Snowflake and Databricks’ marketing teams really brings this to light) when it comes to building your data platform. Think an automatically updating encyclopedia for your data platform.

Building 52

Building BI Data Lake Data Governance 52

Ascend.io

AUGUST 31, 2023

For example, unlike traditional platforms with set schemas, data lakes adapt to frequently changing data structures at points where the data is loaded , accessed, and used. These fluid conditions require unstructured data environments that natively operate with constantly changing formats, data structures, and data semantics.

Data Lake 52

Data Lake Data Warehouse ETL Tools Data Pipeline 52

ProjectPro

NOVEMBER 30, 2021

Cloud: Technology advancements, information security threats, faster internet speeds, and a push to prevent data loss have contributed to the move toward cloud-native storage and processing. It is the most feasible option when the data size is huge. When making instant backups, this can be useful.

Process 52

Process Data Warehouse Data Pipeline AWS 52

AltexSoft

MARCH 30, 2023

Source: Databricks Delta Lake is an open-source, file-based storage layer that adds reliability and functionality to existing data lakes built on Amazon S3, Google Cloud Storage, Azure Data Lake Storage, Alibaba Cloud, HDFS ( Hadoop distributed file system), and others. Delta Lake integrations.

Scala 64

Scala Data Lake Machine Learning BI 64

ProjectPro

AUGUST 11, 2021

The data warehouse layer consists of the relational database management system (RDBMS) that contains the cleaned data and the metadata, which is data about the data. The RDBMS can either be directly accessed from the data warehouse layer or stored in data marts designed for specific enterprise departments.

Data Lake 52

Data Lake Data Warehouse Cloud Hadoop 52

ProjectPro

AUGUST 24, 2021

Thus, as a learner, your goal should be to work on projects that help you explore structured and unstructured data in different formats. Data Warehousing: Data warehousing utilizes and builds a warehouse for storing data. A data engineer interacts with this warehouse almost on an everyday basis.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

ProjectPro

JULY 30, 2021

What are some popular use cases for cloud computing? Cloud storage - Storage over the internet through a web interface turned out to be a boon. With the advent of cloud storage, customers could only pay for the storage they used. Running an image will create an instance on the cloud.

Cloud Computing 40

Cloud Computing Cloud Amazon Web Services AWS 40

Data Engineering Weekly

DECEMBER 18, 2024

Inspired by the human brain, Neuromorphic chips promise unparalleled energy efficiency and the ability to process unstructured data locally on devices. Cloud-Native and Scalable: These IDEs will be designed to run in the cloud, leveraging the scalability and elasticity of cloud infrastructure.

Data Engineering 113

Data Engineering Data Engineer Engineering Data Lake 113