Altexsoft

SEPTEMBER 9, 2022

In data science , metadata is one of the central aspects: It describes data (including unstructured data streams) fed into a big data analytical platform, capturing, for example, formats, file sizes, source of information, permission details, etc. Alation: support for self-service analytics and BI.

Tools 59

Tools Technical Review Software Review Systems Review 59

Capgemini

NOVEMBER 14, 2022

And they also need to trust that the data is current, relevant, and available. Moving a business forward requires fast access to good data for more applications. But that turns IT and analytics teams into the bottleneck. Prior, he was responsible for Informatica’s Intelligent Data Platform. Easy, right?

Agile 52

Agile Technical Review Systems Review Artificial Intelligence 52

Altexsoft

NOVEMBER 9, 2021

If you want to streamline your procurement and gain more visibility into this process, you have to get hold of available data, analyze it, and extract value to make informed decisions. Procurement is a set of operations related to finding, acquiring, and paying for goods and supplies for business needs. Procurement and its challenges.

Analytics 52

Analytics Software Review Systems Review Technical Review 52

Altexsoft

OCTOBER 29, 2020

Here, we introduce you to ETL testing – checking that the data safely traveled from its source to its destination and guaranteeing its high quality before it enters your Business Intelligence reports. What is Data Engineering: Explaining the Data Pipeline, Data Warehouse, and Data Engineer Role.

Testing 63

Testing Tools Software Review Technical Review 63

Cloudera

DECEMBER 10, 2020

Some data warehousing solutions such as appliances and engineered systems have attempted to overcome these problems, but with limited success. . Recently, cloud-native data warehouses changed the data warehousing and business intelligence landscape.

Data 98

Data Technical Review Storage Systems Review 98

CIO

AUGUST 18, 2022

Previously, Walgreens was attempting to perform that task with its data lake but faced two significant obstacles: cost and time. Those challenges are well-known to many organizations as they have sought to obtain analytical knowledge from their vast amounts of data. You can intuitively query the data from the data lake.

Data 348

Data Artificial Inteligence Technical Advisors Technical Review 348

Altexsoft

FEBRUARY 10, 2021

Similar to how DevOps once reshaped the software development landscape, another evolving methodology, DataOps, is currently changing Big Data analytics — and for the better. DataOps is a relatively new methodology that knits together data engineering, data analytics, and DevOps to deliver high-quality data products as fast as possible.

Analytics 75

Analytics DevOps Development Software Review 75

InfoWorld

FEBRUARY 9, 2024

Microsoft Fabric is an end-to-end, software-as-a-service (SaaS) platform for data analytics. It is built around a data lake called OneLake, and brings together new and existing components from Microsoft Power BI, Azure Synapse, and Azure Data Factory into a single integrated environment.

Big Data 84

Big Data Azure Google Cloud Data 84

Altexsoft

MARCH 18, 2021

It is the process of collecting raw data from disparate sources, transmitting it to a staging database for conversion, and loading prepared data into a unified destination system. As data keeps growing in volumes and types, the use of ETL becomes quite ineffective, costly, and time-consuming. Data size and type.

Systems Review 109

Systems Review Technical Review Software Review Compliance 109

Cloudera

JUNE 30, 2022

Today’s general availability announcement covers Iceberg running within key data services in the Cloudera Data Platform (CDP) — including Cloudera Data Warehousing ( CDW ), Cloudera Data Engineering ( CDE ), and Cloudera Machine Learning ( CML ). But the current data lakehouse architectural pattern is not enough.

Data 84

Data Analytics Machine Learning Artificial Inteligence 84

Cloudera

AUGUST 30, 2022

Legacy data sharing involves proliferating copies of data, creating data management, and security challenges. Data quality issues deter trust and hinder accurate analytics. Disparate systems create issues with transparency and compliance. Towards Data Science ). Deploying modern data architectures.

Architecture 77

Architecture Data Artificial Inteligence Artificial Intelligence 77

Altexsoft

JULY 19, 2022

She formulated the thesis in 2018 and published her first article “How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh” in 2019. So, to avoid any confusion, please be aware that data mesh is NOT. Some organizational approaches to analytics ownership are explained in our article about data science teams.

Architecture 52

Architecture Data Analytics Data Engineering 52

Mobilunity

NOVEMBER 24, 2021

The demand for specialists who know how to process and structure data is growing exponentially. In most digital spheres, especially in fintech, where all business processes are tied to data processing, a good big data engineer is worth their weight in gold. Who Is an ETL Engineer?

Development 52

Development Storage Recruiting Architecture 52

Cloudera

OCTOBER 25, 2022

Enabling data and analytics in the cloud allows you to have infinite scale and unlimited possibilities to gain faster insights and make better decisions with data. Cloud data lakehouses provide significant scaling, agility, and cost advantages compared to cloud data lakes and cloud data warehouses.

Cloud 52

Cloud Data Firewall AWS 52

Altexsoft

JANUARY 14, 2021

It is usually created and used primarily for data reporting and analysis purposes. Thanks to the capability of data warehouses to get all data in one place, they serve as a valuable business intelligence (BI) tool, helping companies gain business insights and map out future strategies. Architecture.

Backup 115

Backup Azure Software Review Systems Review 115

Cloudera

DECEMBER 7, 2017

Cloud promises many advantages as an environment for machine learning and analytics. Cloud offers elasticity of those resources to efficiently support transient analytics workloads and data pipelines. Yet there is often a dark side to using public cloud service providers’ analytics offerings in the cloud, too.

Data 40

Data Machine Learning Artificial Inteligence Analytics 40

Altexsoft

NOVEMBER 10, 2021

At the same time, it brings structure to data and empowers data management features similar to those in data warehouses by implementing the metadata layer on top of the store. Traditional data warehouse platform architecture. Data lake architecture example. Poor data quality, reliability, and integrity.

Architecture 52

Architecture Data Storage Machine Learning 52

Datavail

MARCH 10, 2022

According to Gartner, only 20 percent of analytics insights will deliver business results through 2022. In other words, 80 percent of companies’ Big Data projects will fail and/or not deliver results. There are many reasons for this failure, but poor (or a complete lack of) data governance strategies is most often to blame.

Government 40

Government Strategy Weak Development Team Company 40

TIBCO - Connected Intelligence

DECEMBER 20, 2021

TIBCO DQ will become the new data quality product family, through an evolution of our current data quality offerings, significantly enhancing current capabilities available throughout the TIBCO data fabric with built-in AI and ML to automate quality, detection, monitoring, and anomaly resolution.

Innovation 26

Innovation Analytics Virtualization Cloud 26

Agile Engine

DECEMBER 27, 2023

From emerging trends to hiring a data consultancy, this article has everything you need to navigate the data analytics landscape in 2024. What is a data analytics consultancy? Big data consulting services 5. 4 types of data analysis 6. Data analytics use cases by industry 7.

Big Data 52

Big Data Analytics Data Analysis 52

Cloudera

MARCH 2, 2021

Streaming analytics is crucial to modern business – it opens up new product opportunities and creates massive operational efficiencies. However, in the typical enterprise, only a small team has the core skills needed to gain access and create value from streams of data. A rare breed. What do you mean by democratizing?

Data 112

Data Weak Development Team Data Engineering Scalability 112

Altexsoft

MARCH 30, 2023

Along with thousands of other data-driven organizations from different industries, the above-mentioned leaders opted for Databrick to guide strategic business decisions. What is Databricks Databricks is an analytics platform with a unified set of tools for data engineering, data management , data science, and machine learning.

Weak Development Team 64

Weak Development Team Machine Learning Artificial Inteligence Software Review 64

Altexsoft

MARCH 14, 2023

It must collect, analyze, and leverage large amounts of customer data from various sources, including booking history from a CRM system, search queries tracked with Google Analytics, and social media interactions. This means that companies don’t necessarily need a large data engineering team. Data democratization.

Data 59

Data Technical Review Software Review Artificial Inteligence 59