AltexSoft

FEBRUARY 11, 2023

In the article, we explore the role of a data architect, discuss the responsibilities and required skills, and share what kind of companies may need such a specialist. What is a data architect? To get a better understanding of a data architect’s role, let’s clear up what data architecture is.

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The Modern Data Company

APRIL 15, 2024

The term “Data Product” has become a buzzword, often misused or overstretched. But at its core, a Data Product is much more than just data. This blend ensures that a Data Product is informative, actionable, and adaptable to various needs.

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AltexSoft

MARCH 2, 2023

As the amount of enterprise data continues to surge, businesses are increasingly recognizing the importance of data governance — the framework for managing an organization’s data assets for accuracy, consistency, security, and effective use. Projections show that the data governance market will expand from $1.81

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Monte Carlo

MAY 14, 2024

At its core, a table format is a sophisticated metadata layer that defines, organizes, and interprets multiple underlying data files. Table formats incorporate aspects like columns, rows, data types, and relationships, but can also include information about the structure of the data itself.

Data Lake 52

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Precisely

JUNE 22, 2023

As the financial services landscape has become more complex and sophisticated, the concept of data quality has evolved to imply a holistic approach that encompasses the overall trustworthiness of data. They have found ways to curate and manage data to instill confidence among decision-makers.

Data Integration 52

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Precisely

JANUARY 25, 2024

To achieve that, IT leaders must understand the various methods and tools available to them, and how to design a manageable roadmap that balances costs, risks, and benefits. You must carefully consider various mainframe functions, including security, system logs, metadata, and COBOL copybooks when moving to the new cloud platform.

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AltexSoft

OCTOBER 8, 2021

What’s more, that data comes in different forms and its volumes keep growing rapidly every day — hence the name of Big Data. The good news is, businesses can choose the path of data integration to make the most out of the available information. Data integration in a nutshell. Data integration process.

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Databand.ai

AUGUST 30, 2023

DataOps , short for data operations, is an emerging discipline that focuses on improving the collaboration, integration, and automation of data processes across an organization. By using DataOps tools, organizations can break down silos, reduce time-to-insight, and improve the overall quality of their data analytics processes.

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Databand.ai

AUGUST 30, 2023

Integrating these principles with data operation-specific requirements creates a more agile atmosphere that supports faster development cycles while maintaining high quality standards. This demands the implementation of advanced data integration techniques, such as real-time streaming ingestion, batch processing, and API-based access.

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Monte Carlo

NOVEMBER 14, 2023

Databricks announced that Delta tables metadata will also be compatible with the Iceberg format, and Snowflake has also been moving aggressively to integrate with Iceberg. It is designed to be easily queryable with SQL even for large analytic tables (we’re talking petabytes of data). Limited data type support.

Data Lake 64

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Monte Carlo

NOVEMBER 6, 2023

Also known as data catalogs, data discovery tools are specialized software solutions designed to navigate, identify, and visualize data sources within an organization. Here’s an overview of ten popular data discovery tools (in no particular order) that are available today.

Metadata 40

Metadata Unstructured Data Government Data Governance 40

Precisely

OCTOBER 5, 2023

While answers will vary by organization, chances are there’s one commonality: it’s more data than ever before. But what do you do with all that data? Data enrichment is essential to achieving that critical element of context. Data enrichment is essential to achieving that critical element of context.

Raw Data 69

Raw Data Insurance Datasets Telecommunication 69

Cloudera

MARCH 23, 2022

Figure 1: Apache Iceberg fits the next generation data architecture by abstracting storage layer from analytics layer while introducing net new capabilities like time-travel and partition evolution. #1: Apache Iceberg enables seamless integration between different streaming and processing engines while maintaining data integrity between them.

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Metadata Data Architecture BI Machine Learning 90

AltexSoft

AUGUST 22, 2022

In this post, we’ll attempt to explain the idea behind a data fabric, its architectural building blocks, the benefits it brings, and ways to approach its implementation. What is data fabric? to provide a unified view of all enterprise data. Data fabric architecture example. Consolidated data protection.

Architecture 52

Architecture Metadata Data Lake Machine Learning 52

Monte Carlo

AUGUST 25, 2023

This same structure aids in maintaining data quality and simplifies how users interact with and understand the data. One advantage of data warehouses is their integrated nature. As fully managed solutions, data warehouses are designed to offer ease of construction and operation.

Data Lake 52

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AltexSoft

JUNE 26, 2023

Read our article on Hotel Data Management to have a full picture of what information can be collected to boost revenue and customer satisfaction in hospitality. While all three are about data acquisition, they have distinct differences. Data integration , on the other hand, happens later in the data management flow.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

AltexSoft

NOVEMBER 10, 2021

In a nutshell, the lakehouse system leverages low-cost storage to keep large volumes of data in its raw formats just like data lakes. 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.

Architecture 52

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AltexSoft

NOVEMBER 23, 2021

Not to mention that additional sources are constantly being added through new initiatives like big data analytics , cloud-first, and legacy app modernization. To break data silos and speed up access to all enterprise information, organizations can opt for an advanced data integration technique known as data virtualization.

Process 69

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Monte Carlo

AUGUST 30, 2023

Also, remember data governance. Stored test failure data should adhere to your relevant policies or regulations, including data privacy and security, and is only made accessible to the appropriate team members. We could talk data quality all day long. Curious to learn more?

SQL 52

SQL Datasets Database High Quality Data 52

AltexSoft

SEPTEMBER 23, 2021

A data hub is a central mediation point between various data sources and data consumers. It’s not a single technology, but rather an architectural approach that unites storages, data integration and orchestration tools. Storage layer corresponds to the needs of database management and data modeling.

Architecture 52

Architecture Data Lake Unstructured Data Data Warehouse 52

AltexSoft

JULY 29, 2022

a runtime environment (sandbox) for classic business intelligence (BI), advanced analysis of large volumes of data, predictive maintenance , and data discovery and exploration; a store for raw data; a tool for large-scale data integration ; and. a suitable technology to implement data lake architecture.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

ProjectPro

NOVEMBER 15, 2021

Delta Lake Source: Github Delta Lake is an open-source project that allows you to create a Lakehouse design based on data lakes. On top of existing data lakes like S3, ADLS, GCS, and HDFS, Delta Lake enables ACID transactions, scalable metadata handling, and unifies streaming and batch data processing.

Big Data 52

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ProjectPro

AUGUST 24, 2021

But, it is important to wonder how an organization will achieve the same steps on data of different types. The answer is to design a set of standard policies and processes to ensure consistency. The answer is to design a set of standard policies and processes to ensure consistency.

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ProjectPro

JANUARY 31, 2022

Snowflake puts all data on a single high-performance platform by bringing data in from many locations, reducing the complexity and delay imposed by standard ETL processes. Snowflake allows data to be examined and cleaned immediately, assuring data integrity.

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Precisely

FEBRUARY 9, 2024

In 2023, organizations dealt with more data than ever and witnessed a surge in demand for artificial intelligence use cases – particularly driven by generative AI. They relied on their data as a critical factor to guide their businesses to agility and success.

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ProjectPro

NOVEMBER 30, 2021

Transform Data Once the data is validated, we apply transformations to remove duplicates, perform cleansing, standardization, business rule application, perform data integrity checks, data governance, use aggregations, and many more. Stage Data Data that has been transformed is stored in this layer.

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RandomTrees

FEBRUARY 6, 2024

Transformation: Shaping Data for the Future: LLMs facilitate standardizing date formats with precision and translation of complex organizational structures into logical database designs, streamline the definition of business rules, automate data cleansing, and propose the inclusion of external data for a more complete analytical view.

Data Engineering 52

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Cloudera

OCTOBER 7, 2021

In this blog, I will demonstrate the value of Cloudera DataFlow (CDF) , the edge-to-cloud streaming data platform available on the Cloudera Data Platform (CDP) , as a Data integration and Democratization fabric. Introduction to the Data Mesh Architecture. Key Design Principles of a Data Mesh.

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Architecture Metadata Government Kafka 123

Monte Carlo

NOVEMBER 20, 2023

So, what is data quality exactly? As your datasets start to grow beyond what can be manually inspected, there’s a transition to using dbt for more structured data testing. Column-level tests are manually added to validate data integrity and ensure specific data quality standards.

Government 69

Government Data Data Governance Datasets 69

AltexSoft

MAY 12, 2023

Data ingestion tools are software applications or services designed to collect, import, and process data from various sources into a central data storage system or repository. These tools help organizations extract valuable insights, discover hidden patterns, and make informed decisions based on their unstructured data.

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Precisely

OCTOBER 16, 2023

This shift has influenced how cloud resources are designed and marketed, focusing on easy access, modularity, and straightforward deployment. The data-driven enterprise : Another key trend centers around a deeper understanding of data and its vital importance in creating and sustaining competitive advantage.

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The Modern Data Company

JANUARY 22, 2024

The Modern Data Company has been given an honorable mention in Gartner’s 2023 Magic Quadrant for Data Integration. The central piece to this narrative is DataOS®, Modern’s state-of-the-art data operating system designed to unify data across silos on a scalable level.

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Data Integration Metadata Government Unstructured Data 98

The Modern Data Company

JANUARY 22, 2024

The Modern Data Company has been given an honorable mention in Gartner’s 2023 Magic Quadrant for Data Integration. The central piece to this narrative is DataOS®, Modern’s state-of-the-art data operating system designed to unify data across silos on a scalable level.

Data Integration 87

Data Integration Metadata Government Unstructured Data 87

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. Built around a cloud data warehouse, data lake, or data lakehouse. Designed to be modular.

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IT Data Warehouse Data Governance Data Lake 59

The Modern Data Company

MARCH 1, 2024

Instead of forcing all data through a centralized bottleneck, they break it down into modular, self-contained units. Each Data Product is designed for a specific purpose, equipped with the necessary data, transformations, and metadata. Data Products simplify access and processing, empowering faster decision-making.

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phData: Data Engineering

JULY 18, 2023

Central Source of Truth for Analytics A Cloud Data Warehouse (CDW) is a type of database that provides analytical data processing and storage capabilities within a cloud-based infrastructure. This can save your organization significant time and money compared to manual data integration methods.

Data Warehouse 52

Data Warehouse Pipeline-centric Government Data 52

AltexSoft

MARCH 30, 2023

Databricks architecture Databricks provides an ecosystem of tools and services covering the entire analytics process — from data ingestion to training and deploying machine learning models. Besides that, it’s fully compatible with various data ingestion and ETL tools. And here are several more reasons in favor of this choice.

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