Hepta Analytics

FEBRUARY 14, 2022

This week, we got to think about our data ingestion design. We looked at the following: How do we ingest – ETL vs ELT Where do we store the data – Data lake vs data warehouse Which tool to we use to ingest – cronjob vs workflow engine NOTE : This weeks task requires good internet speed and good compute.

Data Ingestion 52

Data Ingestion Data Engineering Data Engineer Engineering 52

Knowledge Hut

MARCH 7, 2024

The greatest data processing challenge of 2024 is the lack of qualified data scientists with the skill set and expertise to handle this gigantic volume of data. Inability to process large volumes of data Out of the 2.5 quintillion data produced, only 60 percent workers spend days on it to make sense of it.

Big Data 98

Big Data Bytes Data Governance Raw Data 98

Christophe Blefari

APRIL 21, 2023

Analytics engineering future This week Tristan Handy—dbt Labs CEO—wrote a post about the future of analytics engineering: The next big step forwards for analytics engineering. A lot of data teams embraced dbt, or at least the SQL with engineering practices to transform data in cloud data warehouses.

Raw Data 130

Raw Data Data Datasets SQL 130

Knowledge Hut

FEBRUARY 27, 2023

A data engineer is a key member of an enterprise data analytics team and is responsible for handling, leading, optimizing, evaluating, and monitoring the acquisition, storage, and distribution of data across the enterprise. Data Engineers indulge in the whole data process, from data management to analysis.

Data Engineering 52

Data Engineering Data Engineer Database-centric Pipeline-centric 52

Ascend.io

NOVEMBER 21, 2023

The emergence of cloud data warehouses, offering scalable and cost-effective data storage and processing capabilities, initiated a pivotal shift in data management methodologies. Extract The initial stage of the ELT process is the extraction of data from various source systems. What Is ELT? So, what exactly is ELT?

Raw Data 52

Raw Data Data Warehouse Data Cleanse Data Integration 52

Cloud Academy

JANUARY 27, 2022

Data engineering is the process of designing and implementing solutions to collect, store, and analyze large amounts of data. The data then gets prepared in formats to be used by people such as business analysts, data analysts, and data scientists. What does a data engineer do?

Certification 52

Certification Data Engineering Data Engineer Engineering 52

Ascend.io

AUGUST 31, 2023

Secondly , the rise of data lakes that catalyzed the transition from ELT to ELT and paved the way for niche paradigms such as Reverse ETL and Zero-ETL. Still, these methods have been overshadowed by EtLT — the predominant approach reshaping today’s data landscape.

Data Lake 52

Data Lake ETL Tools Data Warehouse Data Pipeline 52

Knowledge Hut

DECEMBER 28, 2023

Its comprehensive suite of services can handle data at scale. It’s no surprise that the demand for certified Azure data engineers has skyrocketed. Today, Azure Data Engineer certification is an invaluable asset for those looking to excel in the field of data engineering. Who is an Azure Data Engineer?

Certification 52

Certification Data Engineering Data Engineer Engineering 52

DataKitchen

OCTOBER 5, 2021

A DataOps Engineer owns the assembly line that’s used to build a data and analytic product. While car companies lowered costs using mass production, companies in 2021 put data engineers and data scientists on the assembly line. Imagine if a car company asked the engineers who designed cars to also build them.

Engineering 62

Engineering Raw Data SQL Data Engineering 62

Data Engineering Podcast

DECEMBER 11, 2021

In this episode he explains how it can be used to build rich and useful profiles of your users, the system architecture that powers it, and some of the ways that it is being integrated into an organization’s broader data ecosystem. Missing data? Start trusting your data with Monte Carlo today! Stale dashboards?

Data Warehouse 100

Data Warehouse Raw Data Data Lake BI 100

Ascend.io

JANUARY 2, 2024

The key differentiation lies in the transformational steps that a data pipeline includes to make data business-ready. Ultimately, the core function of a pipeline is to take raw data and turn it into valuable, accessible insights that drive business growth. best suit our processed data? cleaning, formatting)?

Data Pipeline 52

Data Pipeline Building Raw Data Data Warehouse 52

Hevo

MARCH 31, 2023

dbt is a data transformation tool used by data engineers to process raw data within data warehouses. While dbt is a robust tool for transformation, you should be aware of the dbt commands to harness the power of dbt. Mastering dbt commands will let you run complex asks on dbt projects.

Raw Data 52

Raw Data Data Warehouse Data Engineering Data Engineer 52

ProjectPro

JANUARY 19, 2022

Data Engineering is gradually becoming a popular career option for young enthusiasts. Explore this page further and learn everything about data engineers to find the answer. We will cover it all, from its definition, skills, responsibilities to the significance of data engineer in an institution.

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 52

Cloud Academy

JUNE 7, 2022

The data extraction process The first step of modeling and using data begins with extracting it from different sources and putting it in a library where it can be assessed: the Data Warehouse. In some cases, raw data is extracted and placed in the staging area; in others, a few transformations need to be performed.

Cloud 52

Cloud Data Warehouse Raw Data Business Intelligence 52

Cloudera

JANUARY 20, 2021

Most of what is written though has to do with the enabling technology platforms (cloud or edge or point solutions like data warehouses) or use cases that are driving these benefits (predictive analytics applied to preventive maintenance, financial institution’s fraud detection, or predictive health monitoring as examples) not the underlying data.

Manufacturing 109

Manufacturing Data Warehouse Kafka Retail 109

DataKitchen

AUGUST 19, 2021

Each data source is updated on its own schedule, for example, daily, weekly or monthly. The DataKitchen Platform ingests data into a data lake and runs Recipes to create a data warehouse leveraged by users and self-service data analysts. Pharma Data Mesh Domains. The new Recipes run, and BOOM!

Pharmaceutical 98

Pharmaceutical Data Lake Data Warehouse Raw Data 98

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

Analytics Vidhya

FEBRUARY 25, 2023

The need for a data lake arises from the growing volume, variety, and velocity of data companies need to manage and analyze.

Cloud Storage 157

Cloud Storage Data Lake Cloud Unstructured Data 157

Knowledge Hut

JANUARY 18, 2024

A data scientist’s job description focuses on the following – Automating the collection process and identifying the valuable data. Collection of large amounts of raw data, facts, and figures and converting them into a usable format Use of data-driven techniques to solve business-related issues and problems.

Data Science 98

Data Science Business Analyst ETL Method Data Architect 98

Workfall

SEPTEMBER 18, 2023

Meet Airbyte, the data magician that turns integration complexities into child’s play. In this digital era, businesses thrive on data, and making this data dance harmoniously with your analytics tools is crucial. Airbyte ensures that you don’t miss out on those insights due to tangled data integration processes.

Data Pipeline 93

Data Pipeline Raw Data Data Schemas Healthcare 93

ProjectPro

JUNE 9, 2021

This blog on Data Science vs. Data Engineering presents a detailed comparison between the two domains. vs. What does a Data Engineer do? Are you a Data Scientist or a Data Engineer? Is data engineering more important than data science?

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 52

ProjectPro

FEBRUARY 16, 2023

ETL is a critical component of success for most data engineering teams, and with teams harnessing it with the power of AWS, the stakes are higher than ever. Data Engineers and Data Scientists require efficient methods for managing large databases, which is why centralized data warehouses are in high demand.

AWS 52

AWS Data Management ETL Tools Management 52

Knowledge Hut

JANUARY 29, 2024

In today's data-driven world, where information reigns supreme, businesses rely on data to guide their decisions and strategies. However, the sheer volume and complexity of raw data from various sources can often resemble a chaotic jigsaw puzzle.

Raw Data 52

Raw Data Data Mining Data Preparation Structured Data 52

Workfall

JULY 4, 2023

Reading Time: 8 minutes In the world of data engineering, a mighty tool called DBT (Data Build Tool) comes to the rescue of modern data workflows. Imagine a team of skilled data engineers on an exciting quest to transform raw data into a treasure trove of insights.

Data Warehouse 69

Data Warehouse SQL PostgreSQL Database 69

Monte Carlo

APRIL 24, 2023

By accommodating various data types, reducing preprocessing overhead, and offering scalability, data lakes have become an essential component of modern data platforms , particularly those serving streaming or machine learning use cases. See our post: Data Lakes vs. Data Warehouses.

Data Lake 52

Data Lake Google Cloud Data Warehouse AWS 52

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

The Modern Data Company

MAY 10, 2023

In our previous post, The Pros and Cons of Leading Data Management and Storage Solutions , we untangled the differences among data lakes, data warehouses, data lakehouses, data hubs, and data operating systems. Data lakes offer a scalable and cost-effective solution. only structured data).

Data Management 52

Data Management Data Lake Management Data Warehouse 52

Data Engineering Podcast

DECEMBER 16, 2019

Even if you already have a metadata repository this is worth a listen to learn more about the value that visibility of your data can bring to your organization. You work hard to make sure that your data is clean, reliable, and reproducible throughout the ingestion pipeline, but what happens when it gets to the data warehouse?

Metadata 100

Metadata PostgreSQL Datasets Data Warehouse 100

Monte Carlo

MARCH 10, 2022

Monte Carlo’s Barr Moses sat down with Snowflake Director of Product Management Chris Child to talk about building data platforms at scale, how awesome data teams approach data quality, the role of data observability tools in the modern data stack, and more. Enjoy the glimpse into the future.

Engineering 52

Engineering Management Raw Data Scala 52

The Modern Data Company

MAY 10, 2023

In our previous post, The Pros and Cons of Leading Data Management and Storage Solutions , we untangled the differences among data lakes, data warehouses, data lakehouses, data hubs, and data operating systems. Data lakes offer a scalable and cost-effective solution. only structured data).

Data Management 52

Data Management Management Data Lake Data Warehouse 52

The Modern Data Company

MAY 10, 2023

In our previous post, The Pros and Cons of Leading Data Management and Storage Solutions , we untangled the differences among data lakes, data warehouses, data lakehouses, data hubs, and data operating systems. Data lakes offer a scalable and cost-effective solution. only structured data).

Data Management 52

Data Management Management Data Lake Data Warehouse 52

DareData

JULY 5, 2023

If you work at a relatively large company, you've seen this cycle happening many times: Analytics team wants to use unstructured data on their models or analysis. For example, an industrial analytics team wants to use the logs from raw data. The Data Warehouse(s) facilitates data ingestion and enables easy access for end-users.

Data Lake 52

Data Lake Building Raw Data ETL Tools 52

ProjectPro

DECEMBER 7, 2021

Generally, data pipelines are created to store data in a data warehouse or data lake or provide information directly to the machine learning model development. Keeping data in data warehouses or data lakes helps companies centralize the data for several data-driven initiatives.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

Towards Data Science

MAY 3, 2023

Data Engineering Learn about slow change dimensions (SCD) and how to implement SCD Type 2 in VDK Photo by Joshua Sortino on Unsplash Data is the backbone of any organization, and in today’s fast-paced world, it is crucial to keep track of its versions. They store and manage current and historical data in a data warehouse.

Data Lake 87

Data Lake Data SQL Data Warehouse 87

Ascend.io

FEBRUARY 23, 2024

Most organizations, large and small, have complex data environments. This means data lives in multiple places, with a data warehouse possibly at the core, supplemented by ETL processes that funnel data into lakes, lakehouses, or warehouses.

Cloud Storage 52

Cloud Storage Data Ingestion Machine Learning Algorithm 52

Knowledge Hut

JANUARY 30, 2024

In today's world, where data rules the roost, data extraction is the key to unlocking its hidden treasures. As someone deeply immersed in the world of data science, I know that raw data is the lifeblood of innovation, decision-making, and business progress. What is data extraction?

ETL Tools 52

ETL Tools Database-centric Data Mining Data Cleanse 52

Striim

NOVEMBER 17, 2023

Striim serves as a real-time data integration platform that seamlessly and continuously moves data from diverse data sources to destinations such as cloud databases, messaging systems, and data warehouses, making it a vital component in modern data architectures. DMS_SAMPLE.striim_bq_model`,(SELECT 5.1

Machine Learning 52

Machine Learning Data Process PostgreSQL Process 52