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

Knowledge Hut

DECEMBER 26, 2023

According to the World Economic Forum, the amount of data generated per day will reach 463 exabytes (1 exabyte = 10 9 gigabytes) globally by the year 2025. Thus, almost every organization has access to large volumes of rich data and needs “experts” who can generate insights from this rich data.

Data Science 97

Data Science BI Business Intelligence Data Mining 97

Snowflake

APRIL 5, 2023

Two different data modeling approaches—dimensional data modeling and Data Vault—each have their own pros and cons. Modernizing a data warehouse with Snowflake Data Cloud is a smart investment that can provide significant benefits to businesses of all sizes, today more than ever as data models become ever more complex.

Data Warehouse 52

Data Warehouse Healthcare Unstructured Data Metadata 52

Ascend.io

AUGUST 31, 2023

In the dynamic world of data, many professionals are still fixated on traditional patterns of data warehousing and ETL, even while their organizations are migrating to the cloud and adopting cloud-native data services. This unchanging schema forms the foundation for all queries and business intelligence.

Data Lake 52

Data Lake ETL Tools Data Warehouse Data Pipeline 52

Knowledge Hut

JUNE 23, 2023

You must develop predictive models to help industries and businesses make data-driven decisions. Steps to Become a Data Engineer One excellent point is that you don’t need to enter the industry as a data engineer. Data warehousing to aggregate unstructured data collected from multiple sources.

Data Engineering 52

Data Engineering Data Engineer Engineering Non-relational Database 52

Monte Carlo

AUGUST 31, 2023

Data is a priority for your CEO, as it often is for digital-first companies, and she is fluent in the latest and greatest business intelligence tools. Your CTO is excited about migrating to the cloud, and constantly sends your team articles highlighting performance measurements against some of the latest technologies.

Data Lake 52

Data Lake Data Pipeline Unstructured Data Data Warehouse 52

phData: Data Engineering

SEPTEMBER 19, 2023

With the amount of data companies are using growing to unprecedented levels, organizations are grappling with the challenge of efficiently managing and deriving insights from these vast volumes of structured and unstructured data.

Data Lake 52

Data Lake Process Metadata Data Warehouse 52

Rockset

JULY 29, 2022

Enterprise data warehouses (EDWs) became necessary in the 1980s when organizations shifted from using data for operational decisions to using data to fuel critical business decisions. Data warehouses are popular because they help break down data silos and ensure data consistency.

Data Analytics 52

Data Analytics Data Warehouse Datasets Google Cloud 52

Silectis

JANUARY 3, 2021

If you’re new to data engineering or are a practitioner of a related field, such as data science, or business intelligence, we thought it might be helpful to have a handy list of commonly used terms available for you to get up to speed. Big Data Large volumes of structured or unstructured data.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

Knowledge Hut

JUNE 20, 2023

Skills A data engineer should have good programming and analytical skills with big data knowledge. A machine learning engineer should know deep learning, scaling on the cloud, working with APIs, etc. Examples Pull daily tweets from the data warehouse hive spreading in multiple clusters.

Machine Learning 52

Machine Learning Data Engineering Data Engineer Engineering 52

Monte Carlo

JUNE 28, 2022

Over the past decade, Databricks and Apache Spark™ not only revolutionized how organizations store and process their data, but they also expanded what’s possible for data teams by operationalizing data lakes at an unprecedented scale across nearly infinite use cases. billion in 2020 to 17.60

Data Lake 52

Data Lake Metadata AWS Data Warehouse 52

Knowledge Hut

JANUARY 30, 2024

This data can be structured, semi-structured, or entirely unstructured, making it a versatile tool for collecting information from various origins. The extracted data is then duplicated or transferred to a designated destination, often a data warehouse optimized for Online Analytical Processing (OLAP).

ETL Tools 52

ETL Tools Database-centric Data Mining Data Cleanse 52

ProjectPro

DECEMBER 7, 2021

In broader terms, two types of data -- structured and unstructured data -- flow through a data pipeline. The structured data comprises data that can be saved and retrieved in a fixed format, like email addresses, locations, or phone numbers. What is a Big Data Pipeline?

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

Knowledge Hut

JUNE 26, 2023

Hundreds of datasets are available from these two cloud services, so you may practise your analytical skills without having to scrape data from an API. Source: Use Stack Overflow Data for Analytic Purposes 4. We can clean the data, convert the data, and aggregate the data using dbt so that it is ready for analysis.

Data Engineering 98

Data Engineering Data Engineer Coding Project 98

Databand.ai

DECEMBER 13, 2022

We’d be remiss not to share that Joseph was a recent guest on Databand’s MAD Data Podcast , where he discussed ways to keep data systems from becoming unwieldy and shared tips for data teams to manage their data warehouses and keep data pipelines running reliably. You can also watch the video recording.

Data Engineering 52

Data Engineering Data Engineer Engineering AWS 52

AltexSoft

FEBRUARY 11, 2023

It serves as a foundation for the entire data management strategy and consists of multiple components including data pipelines; , on-premises and cloud storage facilities – data lakes , data warehouses , data hubs ;, data streaming and Big Data analytics solutions ( Hadoop , Spark , Kafka , etc.);

Data Architect 87

Data Architect Certification Generalist Big Data 87

Snowflake

NOVEMBER 2, 2023

Over the years, the technology landscape for data management has given rise to various architecture patterns, each thoughtfully designed to cater to specific use cases and requirements. These patterns include both centralized storage patterns like data warehouse , data lake and data lakehouse , and distributed patterns such as data mesh.

Data Lake 89

Data Lake Data Warehouse Cloud Unstructured Data 89

Monte Carlo

AUGUST 25, 2023

At the same time, 81% of IT leaders say their C-suite has mandated no additional spending or a reduction of cloud costs. Data teams need to balance the need for robust, powerful data platforms with increasing scrutiny on costs. But, the options for data storage are evolving quickly. Let’s dive in.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

U-Next

SEPTEMBER 7, 2022

The terms “ Data Warehouse ” and “ Data Lake ” may have confused you, and you have some questions. Structuring data refers to converting unstructured data into tables and defining data types and relationships based on a schema. What is Data Warehouse? .

Data Lake 52

Data Lake Data Warehouse Unstructured Data Amazon Web Services 52

Data Engineering Podcast

JUNE 26, 2022

Push information about data freshness and quality to your business intelligence, automatically scale up and down your warehouse based on usage patterns, and let the bots answer those questions in Slack so that the humans can focus on delivering real value. Unstruk is the DataOps platform for your unstructured data.

Datasets 130

Datasets Unstructured Data Metadata MongoDB 130

ProjectPro

JUNE 9, 2021

Data Science looks into boosting the performance of a machine learning model. Data Engineering handles the entire data pipeline's optimization and efficiency for sourcing data from the data warehouse. It entails generating data visualizations and charts for analysis. Who is a Data Scientist?

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 52

Knowledge Hut

DECEMBER 26, 2023

Let us first get a clear understanding of why Data Science is important. What is the need for Data Science? If we look at history, the data that was generated earlier was primarily structured and small in its outlook. A simple usage of Business Intelligence (BI) would be enough to analyze such datasets.

Data Engineering 98

Data Engineering Data Engineer Engineering Pipeline-centric 98

Monte Carlo

FEBRUARY 15, 2023

Since the inception of the cloud, there has been a massive push to store any and all data. On the surface, the promise of scaling storage and processing is readily available for databases hosted on AWS RDS, GCP cloud SQL and Azure to handle these new workloads. Cloud data warehouses solve these problems.

Data Warehouse 52

Data Warehouse Unstructured Data AWS Business Intelligence 52

Edureka

FEBRUARY 9, 2023

Azure Data Engineering is a rapidly growing field that involves designing, building, and maintaining data processing systems using Microsoft Azure technologies. As a certified Azure Data Engineer, you have the skills and expertise to design, implement and manage complex data storage and processing solutions on the Azure cloud platform.

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 52

Monte Carlo

JUNE 14, 2023

Some organizations choose to still leverage an ETL pattern in the cloud, particularly for production pipelines where data contracts can help reduce data downtime. With cost and physical compute/storage limitations largely lifted, data engineers started to optimize data pipeline architecture for speed and agility.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

AltexSoft

DECEMBER 23, 2022

It is a data integration process with which you first extract raw information (in its original formats) from various sources and load it straight into a central repository such as a cloud data warehouse , a data lake , or a data lakehouse where you transform it into suitable formats for further analysis and reporting.

Process 52

Process Building Raw Data Data Lake 52

Monte Carlo

MARCH 24, 2023

And not just that, many organizations are now even launching cloud to cloud migrations as they consolidate services or optimize their infrastructure. On the face of it, data migration should be fairly straightforward – just pick up your data and move it where it needs to go. How will you communicate with your end users?

Data Warehouse 52

Data Warehouse AWS Cloud Data Governance 52

AltexSoft

JUNE 26, 2023

Data collection is a methodical practice aimed at acquiring meaningful information to build a consistent and complete dataset for a specific business purpose — such as decision-making, answering research questions, or strategic planning. Key differences between structured, semi-structured, and unstructured data.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

AltexSoft

AUGUST 4, 2021

Instead of combing through the vast amounts of all organizational data stored in a data warehouse, you can use a data mart — a repository that makes specific pieces of data available quickly to any given business unit. What is a data mart? Data mart vs data warehouse vs data lake vs OLAP cube.

Data Lake 64

Data Lake Data Warehouse ETL Tools Telecommunication 64

Knowledge Hut

SEPTEMBER 26, 2023

Microsoft Azure, also known as Azure, is a well-known cloud computing service offered by Microsoft. It offers a wide range of services, including computing, storage, databases, machine learning, and analytics, making it a versatile choice for businesses looking to harness the power of the cloud. What is Azure Synapse?

Data Lake 52

Data Lake Database-centric Pipeline-centric Machine Learning 52

ProjectPro

JANUARY 27, 2023

Top ETL Business Use Cases for Streamlining Data Management Data Quality - ETL tools can be used for data cleansing, validation, enriching, and standardization before loading the data into a destination like a data lake or data warehouse.

BI 52

BI ETL Tools Retail Healthcare 52

Monte Carlo

JUNE 26, 2023

billion in cumulative rewards to its users— Head of Data Jeff Hepburn and his team rely on Monte Carlo to deliver end-to-end visibility into the health of their data pipelines from ingestion in Databricks right down to the business intelligence layer. At Ibotta—a cash back rewards platform that has delivered more than $1.1

Data Lake 52

Data Lake Metadata Bytes Google Cloud 52

AltexSoft

NOVEMBER 10, 2021

The pun being obvious, there’s more to that than just a new term: Data lakehouses combine the best features of both data lakes and data warehouses and this post will explain this all. What is a data lakehouse? Data warehouse vs data lake vs data lakehouse: What’s the difference.

Architecture 52

Architecture Data Lake Data Warehouse Metadata 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

JANUARY 24, 2023

With the global cloud data warehousing market likely to be worth $10.42 billion by 2026, cloud data warehousing is now more critical than ever. Cloud data warehouses offer significant benefits to organizations, including faster real-time insights, higher scalability, and lower overhead expenses.

Bytes 52

Bytes Google Cloud Data Warehouse Datasets 52

ProjectPro

SEPTEMBER 26, 2021

Many organizations are willing to pay 20-30% more to their Data Engineers than to Data Scientists. Google Trends shows the large-scale demand and popularity of Big Data Engineer compared with other similar roles, such as IoT Engineer, AI Programmer, and Cloud Computing Engineer. Who is a Big Data Engineer?

Big Data 52

Big Data Data Engineering Data Engineer Engineering 52

ProjectPro

NOVEMBER 30, 2021

In extract-transform-load (ETL), data is obtained from multiple sources, transformed, and stored in a single data warehouse, with access to data analysts , data scientists , and business analysts for data visualization and statistical analysis model building, forecasting, etc.

Process 52

Process Data Pipeline Data Warehouse AWS 52