Knowledge Hut

FEBRUARY 27, 2023

Data Engineers indulge in the whole data process, from data management to analysis. Engineers work with Data Scientists to help make the most of the data they collect and have deep knowledge of distributed systems and computer science. Who is Data Engineer, and What Do They Do?

Data Engineering 52

Data Engineering Data Engineer Database-centric Pipeline-centric 52

Knowledge Hut

SEPTEMBER 26, 2023

Organisations are constantly looking for robust and effective platforms to manage and derive value from their data in the constantly changing landscape of data analytics and processing. These platforms provide strong capabilities for data processing, storage, and analytics, enabling companies to fully use their data assets.

Data Lake 52

Data Lake Database-centric Pipeline-centric Machine Learning 52

Snowflake

DECEMBER 19, 2023

“The Snowflake Native App Framework really helps them give their customers the reassurance that their data is not traveling across the internet, and that they’re able to do all of their data processing within their own environment.” One conversation quickly coming to the forefront is first-party data.

Building 79

Building Pipeline-centric Database-centric Digital Media 79

Knowledge Hut

DECEMBER 26, 2023

Data Engineers are skilled professionals who lay the foundation of databases and architecture. Using database tools, they create a robust architecture and later implement the process to develop the database from zero. Let us now understand the basic responsibilities of a Data engineer.

Data Engineering 76

Data Engineering Data Engineer Engineering Pipeline-centric 76

Towards Data Science

JULY 13, 2023

The example we’ll walk you through will mirror a typical LLM application workflow you’d run to populate a vector database with some text knowledge. Specifically, we’ll cover pulling data from the web, creating text embeddings (vectors) and pushing them to a vector store. The application will receive a small data input (e.g.,

Building 73

Building Database-centric Database Coding 73

Maxime Beauchemin

MARCH 8, 2021

For those reasons, it is not surprising that it has taken over most of the modern data stack: infrastructure, databases, orchestration, data processing, AI/ML and beyond. That’s without mentioning the fact that for a cloud-native company, Tableau’s Windows-centric approach at the time didn’t work well for the team.

Business Intelligence 130

Business Intelligence BI Database-centric Google Cloud 130

Ascend.io

SEPTEMBER 14, 2023

Data engineers can find one for almost any need, from data extraction to complex transformations, ensuring that they’re not reinventing the wheel by writing code that’s already been written. PySpark, for instance, optimizes distributed data operations across clusters, ensuring faster data processing.

Data Engineering 52

Data Engineering Data Engineer Python Engineering 52

Ascend.io

AUGUST 29, 2023

In this article, we’ll break down the intricacies of an end-to-end data pipeline and highlight its importance in today’s landscape. A visual maze: The tangled web of disparate tools commonly used in fragmented data pipelines. Playing the Field – Data Transformation: This is where the action happens.

Data Pipeline 52

Data Pipeline Pipeline-centric Database-centric Data Ingestion 52

Knowledge Hut

NOVEMBER 17, 2023

The demand for data-related professions, including data engineering, has indeed been on the rise due to the increasing importance of data-driven decision-making in various industries. Becoming an Azure Data Engineer in this data-centric landscape is a promising career choice.

Data Engineering 52

Data Engineering Data Engineer Engineering Scala 52

Ascend.io

JUNE 8, 2023

Treating data as a product is more than a concept; it’s a paradigm shift that can significantly elevate the value that business intelligence and data-centric decision-making have on the business. It is the stage where data truly becomes a product, delivering tangible value to its end users.

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 52

Knowledge Hut

DECEMBER 27, 2023

Variety : Refers to the professed formats of data, from structured, numeric data in traditional databases, to unstructured text documents, emails, videos, audios, stock ticker data and financial transactions. Some examples of Big Data: 1. However, big data analytics and using big data tools must be learned.

Big Data Tools 59

Big Data Tools Big Data Hadoop Database-centric 59

Databand.ai

JULY 19, 2023

This capability is particularly useful in complex data landscapes, where data may pass through multiple systems and transformations before reaching its final destination Impact analysis: When changes are made to data sources or data processing systems, it’s critical to understand the potential impact on downstream processes and reports.

Pipeline-centric 52

Pipeline-centric Data Governance Metadata Government 52

Dataquest

JANUARY 25, 2017

But what about data engineers? A data scientist is only as good as the data they have access to. Most companies store their data in variety of formats across databases and text files. This is where data engineers come in — they build pipelines that transform that data into formats that data scientists can use.

Data Engineering 52

Data Engineering Data Engineer Pipeline-centric Database-centric 52

Maxime Beauchemin

JANUARY 20, 2017

The fact that ETL tools evolved to expose graphical interfaces seems like a detour in the history of data processing, and would certainly make for an interesting blog post of its own. Sure, there’s a need to abstract the complexity of data processing, computation and storage.

Data Engineering 130

Data Engineering Data Engineer Engineering ETL Tools 130

Knowledge Hut

JULY 3, 2023

Studying data in deeper detail can help to identify the inefficiencies, bottlenecks or anomalies hence leading to quick actions resulting in efficient operations and reduced costs. Appreciated Customer Experience: The industry focuses on customer-centric approaches to enhance the overall customer experience.

Data Ingestion 52

Data Ingestion Pipeline-centric Google Cloud Media 52

Ascend.io

MAY 31, 2023

Data Transformation Because of the many variations of source systems, the data collected during the ingestion phase is often raw, messy, and unstructured. In the ETL world, data transformation is intended to change the structure of the source data to match a specific target database schema, usually in the context of a data warehouse.

Data Pipeline 52

Data Pipeline ETL Tools Pipeline-centric Data Warehouse 52

Grouparoo

OCTOBER 6, 2021

36 Give Data Products a Frontend with Latent Documentation Document more to help everyone 37 How Data Pipelines Evolve Build ELT at mid-range and move to data lakes when you need scale 38 How to Build Your Data Platform like a Product PM your data with business. Increase visibility. how fast are queries?

Data Engineering 52

Data Engineering Data Engineer Engineering Pipeline-centric 52

Knowledge Hut

OCTOBER 8, 2023

Not only that, but they are also responsible for working on web applications, content management systems, databases, and operating systems. I had earlier chosen KnowledgeHut’s training in Project Management to understand such processes efficiently.

Software Engineer 52

Software Engineer Software Engineering Project Engineering 52

Cloudera

AUGUST 31, 2021

Case Study: Accenture’s Experience on Legacy Data Warehouse Migration into Cloudera with a Health Insurance Company . Business Problem & Background.

Data Warehouse 82

Data Warehouse Database-centric Metadata Cloud 82

ProjectPro

MARCH 19, 2015

Big Data NoSQL databases were pioneered by top internet companies like Amazon, Google, LinkedIn and Facebook to overcome the drawbacks of RDBMS. RDBMS is not always the best solution for all situations as it cannot meet the increasing growth of unstructured data.

NoSQL 49

NoSQL Big Data SQL Database-centric 49

Knowledge Hut

JUNE 26, 2023

Application Management Application management expertise is crucial in an Azure-centric ecosystem. Microsoft Certification: Azure Data Fundamentals Azure Data Fundamentals is designed for individuals who want to gain knowledge of data principles & core concepts related to Azure data services.

Cloud Computing 52

Cloud Computing Certification Pipeline-centric Database-centric 52

AltexSoft

JULY 18, 2023

It allows data scientists to analyze large datasets and interactively run jobs on them from the R shell. Big data processing. Distributed: RDDs are distributed across the network, enabling them to be processed in parallel. Here are some of the possible use cases.

Big Data 52

Big Data Data Process Process Hadoop 52

Confluent

JUNE 18, 2019

As the databases professor at my university used to say, it depends. Using SQL to run your search might be enough for your use case, but as your project requirements grow and more advanced features are needed—for example, enabling synonyms, multilingual search, or even machine learning—your relational database might not be enough.

Architecture 96

Architecture Building Kafka Database-centric 96

AltexSoft

JUNE 7, 2021

Hadoop and Spark are the two most popular platforms for Big Data processing. They both enable you to deal with huge collections of data no matter its format — from Excel tables to user feedback on websites to images and video files. Obviously, Big Data processing involves hundreds of computing units.

Big Data Tools 98

Big Data Tools Hadoop Big Data Database-centric 98

Confluent

FEBRUARY 13, 2019

Akka Streams then changed tact as streaming became the core mechanism to drive processors in a more data-centric manner. We think of streams and events much like database tables and rows; they are the basic building blocks of a data platform. The term is called turning the database inside out.

Architecture 90

Architecture Programming Kafka Database-centric 90

Confluent

SEPTEMBER 25, 2019

Of course, this is not to imply that companies will become only software (there are still plenty of people in even the most software-centric companies), just that the full scope of the business is captured in an integrated software defined process. Here, the bank loan business division has essentially become software.

Database-centric 56

Database-centric Pipeline-centric Kafka Retail 56

Confluent

MAY 9, 2019

Storing events in a stream and connecting streams via stream processors provide a generic, data-centric, distributed application runtime that you can use to build ETL, event streaming applications, applications for recording metrics and anything else that has a real-time data requirement. Instrumentation plane.

Kafka 93

Kafka Pipeline-centric Architecture Database-centric 93

phData: Data Engineering

JULY 18, 2023

First up, let’s dive into the foundation of every Modern Data Stack, a cloud-based data warehouse. Central Source of Truth for Analytics A Cloud Data Warehouse (CDW) is a type of database that provides analytical data processing and storage capabilities within a cloud-based infrastructure.

Data Warehouse 52

Data Warehouse Pipeline-centric Government Data 52