Dataversity

JANUARY 28, 2021

It has been ten years since Pentaho Chief Technology Officer James Dixon coined the term “data lake.” While data warehouse (DWH) systems have had longer existence and recognition, the data industry has embraced the more […]. The post A Bridge Between Data Lakes and Data Warehouses appeared first on DATAVERSITY.

Data Lakes 59

Data Lakes Data Warehouse Data Quality Data Governance 59

Dataversity

AUGUST 19, 2022

The data we produce and manage is growing in scale and demands careful consideration of the proper data framework for the job. There’s no one-size-fits-all data architecture, and […]. The post Data Lakes Are Dead: Evolving Your Company’s Data Architecture appeared first on DATAVERSITY.

Data Lakes 52

Data Lakes Data Governance Data Quality 52

Dataversity

AUGUST 15, 2022

If data is the new oil, then high-quality data is the new black gold. Just like with oil, if you don’t have good data quality, you will not get very far. So, what can you do to ensure your data is up to par and […]. You might not even make it out of the starting gate.

Data Observability 52

Data Observability Data Quality Data Lakes Data Warehouse 52

Data Science Dojo

JULY 6, 2023

These tools provide data engineers with the necessary capabilities to efficiently extract, transform, and load (ETL) data, build data pipelines, and prepare data for analysis and consumption by other applications. Essential data engineering tools for 2023 Top 10 data engineering tools to watch out for in 2023 1.

Data Engineering 195

Data Engineering Data Engineer Data Engineering Data Engineering 195

Data Science Dojo

JANUARY 12, 2023

When it comes to data, there are two main types: data lakes and data warehouses. What is a data lake? An enormous amount of raw data is stored in its original format in a data lake until it is required for analytics applications. Which one is right for your business?

Data Lakes 222

Data Lakes Data Warehouse Hadoop Machine Learning 222

Data Science Connect

JULY 28, 2023

This data is then integrated into centralized databases for further processing and analysis. Data Cleaning and Preprocessing IoT data can be noisy, incomplete, and inconsistent. Data engineers employ data cleaning and preprocessing techniques to ensure data quality, making it ready for analysis and decision-making.

Internet of Things 130

Internet of Things Data Engineering Data Engineer Data Engineering 130

Dataversity

OCTOBER 4, 2022

The rise of data lakes and adjacent patterns such as the data lakehouse has given data teams increased agility and the ability to leverage major amounts of data. Constantly evolving data privacy legislation and the impact of major cybersecurity breaches has led to the call for responsible data […].

Data Lakes 94

Data Lakes Data Governance Data Quality 94

Dataversity

FEBRUARY 10, 2023

For many of these organizations, the path toward becoming more data-driven lies in the power of data lakehouses, which combine elements of data warehouse architecture with data lakes.

Data Lakes 52

Data Lakes Data Warehouse Data Quality Data Governance 52

Dataversity

APRIL 23, 2021

Within the Data Management industry, it’s becoming clear that the old model of rounding up massive amounts of data, dumping it into a data lake, and building an API to extract needed information isn’t working. Click to learn more about author Brian Platz.

Database 90

Database Data Lakes Data Silos Data Governance 90

Dataversity

SEPTEMBER 12, 2022

The ways in which we store and manage data have grown exponentially over recent years – and continue to evolve into new paradigms. For much of IT history, though, enterprise data architecture has existed as monolithic, centralized “data lakes.” The post Data Mesh or Data Mess?

Data Lakes 52

Data Lakes Data Quality Data Governance Cloud Data 52

Pickl AI

NOVEMBER 15, 2023

Discover the nuanced dissimilarities between Data Lakes and Data Warehouses. Data management in the digital age has become a crucial aspect of businesses, and two prominent concepts in this realm are Data Lakes and Data Warehouses. It acts as a repository for storing all the data.

Data Lakes 52

Data Lakes Data Warehouse Database ETL 52

Dataversity

MAY 19, 2023

In the early days of business analysis and underwriting, data was managed with simply a pen and paper and, of course, Excel spreadsheets. As technology has advanced, databases, warehouses, and data lakes have enabled information to be collected, stored, and managed electronically.

Data Lakes 52

Data Lakes Database Data Quality 52

Precisely

JUNE 6, 2023

The group kicked off the session by exchanging ideas about what it means to have a modern data architecture. Atif Salam noted that as recently as a year ago, the primary focus in many organizations was on ingesting data and building data lakes.

Data Observability 52

Data Observability Data Lakes Data Quality ETL 52

Dataversity

DECEMBER 16, 2020

According to IDC, the size of the global datasphere is projected to reach 163 ZB by 2025, leading to the disparate data sources in legacy systems, new system deployments, and the creation of data lakes and data warehouses. Most organizations do not utilize the entirety of the data […].

Data Warehouse 69

Data Warehouse Data Lakes Data Profiling Data Quality 69

Alation

APRIL 18, 2023

For any data user in an enterprise today, data profiling is a key tool for resolving data quality issues and building new data solutions. In this blog, we’ll cover the definition of data profiling, top use cases, and share important techniques and best practices for data profiling today.

Data Profiling 52

Data Profiling Data Quality Data Governance Data Pipeline 52

ODSC - Open Data Science

FEBRUARY 14, 2023

It also looks at solutions that make it easier for data teams to hire, manage, retain, and execute effectively using modern data tooling — all while gaining that sought-after efficiency. It also addresses the strategies and best practices for implementing a data mesh.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Precisely

MARCH 27, 2023

As part of the project, the company implemented a data governance program and rolled out scalable data quality tools to enhance the overall value it could deliver to its go-to-market organization. After several false starts, the company engaged Precisely Strategic Services to help align its people, processes, and technology.

Data Quality 52

Data Quality Data Lakes Data Governance Data Modeling 52

IBM Journey to AI blog

DECEMBER 16, 2022

For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance. It uses metadata and data management tools to organize all data assets within your organization. Ensuring data quality is made easier as a result.

Data Quality 52

Data Quality Data Governance Data Wrangling Data Scientist 52

IBM Journey to AI blog

MAY 9, 2023

Data: the foundation of your foundation model Data quality matters. An AI model trained on biased or toxic data will naturally tend to produce biased or toxic outputs. When objectionable data is identified, we remove it, retrain the model, and repeat. Data curation is a task that’s never truly finished.

AI 134

AI AI Data Quality Data Lakes 134

Alation

OCTOBER 21, 2021

Data Swamp vs Data Lake. When you imagine a lake, it’s likely an idyllic image of a tree-ringed body of reflective water amid singing birds and dabbling ducks. I’ll take the lake, thank you very much. Many organizations have built a data lake to solve their data storage, access, and utilization challenges.

Data Lakes 52

Data Lakes Data Governance Data Warehouse Business Intelligence 52

Dataconomy

SEPTEMBER 4, 2023

Understand what insights you need to gain from your data to drive business growth and strategy. Best practices in cloud analytics are essential to maintain data quality, security, and compliance ( Image credit ) Data governance: Establish robust data governance practices to ensure data quality, security, and compliance.

Analytics 203

Analytics Analytics Big Data Analytics Big Data Analytics 203

Alation

FEBRUARY 13, 2020

Figure 1 illustrates the typical metadata subjects contained in a data catalog. Figure 1 – Data Catalog Metadata Subjects. Datasets are the files and tables that data workers need to find and access. They may reside in a data lake, warehouse, master data repository, or any other shared data resource.

Data Lakes 64

Data Lakes Data Analysis Data Analysis Big Data 64

ODSC - Open Data Science

OCTOBER 20, 2023

HPCC Systems — The Kit and Kaboodle for Big Data and Data Science Bob Foreman | Software Engineering Lead | LexisNexis/HPCC Join this session to learn how ECL can help you create powerful data queries through a comprehensive and dedicated data lake platform.

AI 52

AI AI Data Science Machine Learning 52

Alation

MARCH 22, 2022

As the latest iteration in this pursuit of high-quality data sharing, DataOps combines a range of disciplines. It synthesizes all we’ve learned about agile, data quality , and ETL/ELT. This produces end-to-end lineage so business and technology users alike can understand the state of a data lake and/or lake house.

DataOps 52

DataOps Data Pipeline Data Engineering Data Engineer 52

ODSC - Open Data Science

APRIL 3, 2023

Skills like effective verbal and written communication will help back up the numbers, while data visualization (specific frameworks in the next section) can help you tell a complete story. Data Wrangling: Data Quality, ETL, Databases, Big Data The modern data analyst is expected to be able to source and retrieve their own data for analysis.

Analytics 52

Analytics Analytics Data Analyst SQL 52

Precisely

JANUARY 19, 2023

Data Integration Enterprises are betting big on analytics, and for good reason. The volume, velocity, and variety of data is growing exponentially. Platforms like Hadoop and Spark prompted many companies to begin thinking about big data differently than they had in the past.

AWS 52

AWS Cloud Computing Data Pipeline Big Data 52

Pickl AI

AUGUST 1, 2023

Key Components of Data Engineering Data Ingestion : Gathering data from various sources, such as databases, APIs, files, and streaming platforms, and bringing it into the data infrastructure. Data Processing: Performing computations, aggregations, and other data operations to generate valuable insights from the data.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

AWS Machine Learning Blog

JANUARY 26, 2024

Also consider using Amazon Security Lake to automatically centralize security data from AWS environments, SaaS providers, on premises, and cloud sources into a purpose-built data lake stored in your account.

AWS 120

AWS AI AI ML 120

Alation

JANUARY 26, 2023

Offer the right tools Data stewardship is greatly simplified when the right tools are on hand. So ask yourself, does your steward have the software to spot issues with data quality, for example? 2) Always Remember Compliance Source: Unsplash There are now many different data privacy and security laws worldwide.

Data Governance 52

Data Governance Data Engineering Data Engineer Data Engineering 52

ODSC - Open Data Science

OCTOBER 15, 2023

Cloudera Cloudera is a cloud-based platform that provides businesses with the tools they need to manage and analyze data. They offer a variety of services, including data warehousing, data lakes, and machine learning.

Machine Learning 52

Machine Learning Machine Learning Data Pipeline AI 52

Alation

NOVEMBER 28, 2022

It’s impossible for data teams to assure the data quality of such spreadsheets and govern them all effectively. If unaddressed, this chaos can lead to data quality, compliance, and security issues. In an enterprise, there may be thousands of spreadsheets used for critical business decisions.

Database 52

Database Data Governance Data Quality Data Lakes 52

The MLOps Blog

MARCH 15, 2023

Data Processing : You need to save the processed data through computations such as aggregation, filtering and sorting. Data Storage : To store this processed data to retrieve it over time – be it a data warehouse or a data lake. This ensures that the data is accurate, consistent, and reliable.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59

Alation

NOVEMBER 2, 2022

“At Databricks, we’re focused on enabling customers to adopt the data lakehouse, and that’s an open data architecture that combines the best of the data warehouse and the data lake into one platform,” Ferguson says. “[The And data governance is critical to driving adoption.”.

Data Governance 98

Data Governance Data Lakes Data Warehouse Analytics 98

Alation

JUNE 14, 2021

According to a 2020 451 Research report , “data catalogs are rapidly building out automated functionality,” including “automated suggestions, automated discovery and tagging, and automated data-quality scoring.” These are essential to enabling a more rapid process of sensitive data discovery.

Data Governance 97

Data Governance Data Lakes Data Classification Data Quality 97

AWS Machine Learning Blog

AUGUST 4, 2023

In the data flow view, you can now see a new node added to the visual graph. For more information on how you can use SageMaker Data Wrangler to create Data Quality and Insights Reports, refer to Get Insights On Data and Data Quality. SageMaker Data Wrangler offers over 300 built-in transformations.

ML 76

ML ML AWS AI 76

DagsHub

FEBRUARY 29, 2024

Only once you form a clear definition and understanding of the business problem , goals, and the necessity of machine learning should you move forward to the next stage of data preparation. In large ML organizations, there is typically a dedicated team for all the above aspects of data preparation.

Data Preparation 52

Data Preparation Machine Learning Machine Learning Data Governance 52

The MLOps Blog

JUNE 27, 2023

Data quality control: Robust dataset labeling and annotation tools incorporate quality control mechanisms such as inter-annotator agreement analysis, review workflows, and data validation checks to ensure the accuracy and reliability of annotations. Data monitoring tools help monitor the quality of the data.

Machine Learning 134

Machine Learning Machine Learning ML ML 134