Precisely

MARCH 14, 2023

Read Quality data you can depend on – today, tomorrow, and beyond For many years Precisely customers have ensured the accuracy of data across their organizations by leveraging our leading data solutions including Trillium Quality, Spectrum Quality, and Data360 DQ+. What does all this mean for your business?

Data Integration 52

Data Integration High Quality Data BI Data 52

Ripple Engineering

JANUARY 29, 2024

  A lack of a centralized system makes building a single source of high-quality data difficult. The key aspect of any business-centric team in delivering products and features is to make critical decisions on ensuring low latency, high throughput, cost-effective storage, and highly efficient infrastructure.

Database-centric 52

Database-centric Pipeline-centric NoSQL High Quality Data 52

Precisely

NOVEMBER 29, 2023

Organizations should be careful not to automate business processes before considering which data sets those processes impact. Automation increases the potential to create a large volume of bad data very quickly. Digital transformation leverages data and new technologies to drive value through innovation and efficiency.

High Quality Data 64

High Quality Data Data Integration Process Building 64

Databand.ai

JULY 17, 2023

By adopting a set of best practices inspired by Agile methodologies, DevOps principles, and statistical process control techniques, DataOps helps organizations deliver high-quality data insights more efficiently.

Data Pipeline 52

Data Pipeline Machine Learning High Quality Data BI 52

Knowledge Hut

APRIL 25, 2024

Data-driven Orientation: Both big data and machine learning embrace a data-centric approach. They prioritize the utilization of data to acquire insights, generate predictions, and inform decision-making. Data Processing: Both big data and machine learning encompass the processing and examination of extensive datasets.

Machine Learning 52

Machine Learning Big Data Unstructured Data Data Mining 52

Monte Carlo

FEBRUARY 20, 2024

Consideration What to Look For Integration Capabilities Support for a diverse array of data sources and destinations, ensuring compatibility with your data ecosystem. Batch vs. Streaming Assess if your data processing leans towards real-time analytics or if batch processing suffices for your use case.

Data Ingestion 52

Data Ingestion Google Cloud Kafka AWS 52

Picnic Engineering

DECEMBER 12, 2023

The rich context provided by our Snowflake-powered Data Warehouse enhances their performance, allowing us to create a robust feature set for training. Training these models on our historical demand and assessing their performance is manageable as a one-time project since concepts like data drift are still not a big concern.

Machine Learning 52

Machine Learning Deep Learning Food High Quality Data 52

DataKitchen

SEPTEMBER 21, 2023

L1 is usually the raw, unprocessed data ingested directly from various sources; L2 is an intermediate layer featuring data that has undergone some form of transformation or cleaning; and L3 contains highly processed, optimized, and typically ready for analytics and decision-making processes. What is Data in Use?

Raw Data 67

Raw Data Data Business Intelligence High Quality Data 67

Knowledge Hut

APRIL 23, 2024

Gathering data at high velocities necessitates capturing and ingesting data streams as they occur, ensuring timely acquisition and availability for analysis. Utilizing is related to the data processing and analyzing speed for gleaning useful insights.

Big Data 52

Big Data Media Datasets Unstructured Data 52

Ascend.io

FEBRUARY 28, 2024

While data engineering and Artificial Intelligence (AI) may seem like distinct fields at first glance, their symbiosis is undeniable. The foundation of any AI system is high-quality data. Here lies the critical role of data engineering: preparing and managing data to feed AI models.

Data Engineering 52

Data Engineering Data Engineer Engineering Metadata 52

Monte Carlo

JULY 27, 2023

These specialists are also commonly referred to as data reliability engineers. To be successful in their role, data quality engineers will need to gather data quality requirements (mentioned in 65% of job postings) from relevant stakeholders.

Engineering 52

Engineering Healthcare Scala Data Warehouse 52

Knowledge Hut

MARCH 5, 2024

Technology According to a Glassdoor report, data engineering average salary at large companies generally ranges from S$86,288 to S$171,980. Data engineers in the technology industry focus on data streaming and data processing pipelines. Size issues are another major data engineering issue for technology companies.

Data Engineering 52

Data Engineering Data Engineer Engineering Education 52

Edureka

FEBRUARY 7, 2023

An Azure Data Engineer is responsible for designing, implementing, and maintaining data management and data processing systems on the Microsoft Azure cloud platform. They work with large and complex data sets and are responsible for ensuring that data is stored, processed, and secured efficiently and effectively.

Data Engineering 52

Data Engineering Data Engineer Engineering Programming Language 52

Knowledge Hut

JUNE 28, 2023

Business Intelligence: Business Intelligence can handle moderate to large volumes of structured data. While it may not be designed specifically for big data processing, it can integrate with data processing technologies to analyze substantial amounts of data.

Data Mining 52

Data Mining Business Intelligence BI Datasets 52

Workfall

JULY 4, 2023

DBT’s superpowers include seamlessly connecting with databases and data warehouses, performing amazing transformations, and effortlessly managing dependencies to ensure high-quality data. Each successful deployment enriches its data ecosystem, empowering decision-makers with valuable, up-to-date insights.

Data Warehouse 69

Data Warehouse SQL PostgreSQL Database 69

DataKitchen

JULY 27, 2023

Azure Databricks Delta Live Table s: These provide a more straightforward way to build and manage Data Pipelines for the latest, high-quality data in Delta Lake. Azure Machine Learning can then use this data to train, test, and deploy machine learning models. It does the job.

Data Pipeline 52

Data Pipeline BI Machine Learning Data Preparation 52

Databand.ai

JULY 5, 2023

Incorporating these features into your data observability strategy will enable you to maintain high-quality data pipelines and make informed decisions about optimizing performance. Notable Data Observability Tools Here are a few notable data observability tools.

Data Pipeline 52

Data Pipeline Data Lake Data Warehouse Datasets 52

Databand.ai

JULY 10, 2023

Databand allows data engineering and data science teams to define data quality rules, monitor data consistency, and identify data drift or anomalies. It also provides real-time notifications and alerts, enabling teams to proactively address issues and maintain high-quality data.

Data Pipeline 52

Data Pipeline Algorithm Raw Data Aggregated Data 52

Monte Carlo

FEBRUARY 21, 2023

Reduced reliance on IT Integral to a data fabric is a set of pre-built models and algorithms that expedite data processing. You can also feel confident that users at your organization will more readily adopt your data fabric, because they’ll know they can trust the insights it generates.

Data Architecture 52

Data Architecture Architecture Metadata Unstructured Data 52

Monte Carlo

FEBRUARY 21, 2023

Reduced reliance on IT Integral to a data fabric is a set of pre-built models and algorithms that expedite data processing. You can also feel confident that users at your organization will more readily adopt your data fabric, because they’ll know they can trust the insights it generates.

Data Architecture 52

Data Architecture Architecture Metadata Unstructured Data 52

DataKitchen

OCTOBER 2, 2023

Providing a comprehensive suite of features, from alerts and analytics to fast integration and data tests, ‘The Traveler’ empowers the End-to-End Data Product Team to manage their data landscape proactively.

Data Ingestion 40

Data Ingestion Data Government Datasets 40

Knowledge Hut

JANUARY 25, 2024

Trustworthy Analytics: Reliable data supports accurate statistical analysis. Enhanced Visualization: Clean data leads to clearer data visualizations. Efficient Machine Learning: High-quality data is vital for training accurate ML models. What is the difference between data cleaning and data transformation?

Data Cleanse 52

Data Cleanse Datasets Data Preparation Data Science 52

Monte Carlo

JANUARY 31, 2023

The problem: over-reliance on manual testing and a lack of visibility across domains Checkout.com’s decentralized data structure and reliance on manual tests and monitors meant that data engineering was a single point of failure for data issues. And I hope that this trend will continue moving forward.”

Data 52

Data High Quality Data Data Engineering Data Engineer 52

Ascend.io

JUNE 8, 2023

Good data stewardship and healthy data catalogs are worthwhile investments. Data Product Costing With the proliferation of data volumes and the continued adoption of AI and ML in business, data processing costs are becoming a large part of company budgets. But how do you unlock these capabilities?

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 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

ProjectPro

FEBRUARY 21, 2023

GCP Data Engineer Certification The Google Cloud Certified Professional Data Engineer certification is ideal for data professionals whose jobs generally involve data governance, data handling, data processing, and performing a lot of feature engineering on data to prepare it for modeling.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

Databand.ai

JANUARY 24, 2023

As a result, DataOps observability is an essential part of maintaining high quality data and ensuring that data practices are as efficient and effective as possible. In doing so, it allows teams to identify problems faster and, therefore, deliver solutions faster. Who’s Involved in a DataOps Team?

Retail 52

Retail Banking Government Manufacturing 52

Zalando Engineering

JULY 17, 2017

In this era of crowdsourcing platforms such as Amazon's Mechanical Turk and CrowdFlower , it makes sense to leverage these platforms to try to create these high quality data sets at a reasonable cost. If you're interested in the intersection of microservices, stream data processing and machine learning, we're hiring.

Machine Learning 40

Machine Learning Data Datasets Data Collection 40

Cloudera

AUGUST 21, 2021

million customers worldwide, recognized how the immense volume of data they maintained could provide better insight into customers’ needs. Since leveraging Cloudera’s data platform, Rabobank has been able to improve its customers’ financial management. Rabobank , headquartered in the Netherlands with over 8.3

Banking 99

Banking Raw Data High Quality Data Business Intelligence 99

AltexSoft

AUGUST 22, 2022

In the data fabric vs data lake dilemma, everything is simple. Data lakes are central repositories that can ingest and store massive amounts of both structured and unstructured data, typically for future analysis, big data processing , and machine learning. A data fabric, on the contrary, doesn’t store data.

Architecture 52

Architecture Metadata Data Lake Machine Learning 52

U-Next

SEPTEMBER 29, 2022

The term “unstructured data sources” encompasses any data that cannot be easily categorized, such as photos, graphics, videos, streaming instrument data, web pages, PDF files, PowerPoint presentations, emails, and data processing documents. However, it lacks the strict structure of a data model.

Management 52

Management Unstructured Data Data Government 52