Striim

SEPTEMBER 11, 2024

Third-Party Data: External data sources that your company does not collect directly but integrates to enhance insights or support decision-making. These data sources serve as the starting point for the pipeline, providing the raw data that will be ingested, processed, and analyzed.

Data Pipeline 98

Data Pipeline Designing Data Lake Data Warehouse 98

Netflix Tech

NOVEMBER 14, 2023

In this context, managing the data, especially when it arrives late, can present a substantial challenge! In this three-part blog post series, we introduce you to Psyberg , our incremental data processing framework designed to tackle such challenges! Raw data for hours 3 and 6 arrive. Let’s dive in!

Data Engineering 108

Data Engineering Data Engineer Engineering Metadata 108

Cloudera

AUGUST 21, 2021

When pandemic lockdowns swept through Indonesia, Bank Mandiri needed to ensure that their systems could integrate data sources to generate insights efficiently while supporting their teams working remotely. million) of transactions per week at each of the bank’s branches.

Banking 103

Banking Raw Data High Quality Data Cloud 103

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. How ELT Works The process of ELT can be broken down into the following three stages: 1. What Is ELT? So, what exactly is ELT?

Raw Data 52

Raw Data Data Warehouse Data Cleanse Data Integration 52

Databand.ai

JULY 6, 2023

7 Data Pipeline Examples: ETL, Data Science, eCommerce, and More Joseph Arnold July 6, 2023 What Are Data Pipelines? Data pipelines are a series of data processing steps that enable the flow and transformation of raw data into valuable insights for businesses.

Data Pipeline 52

Data Pipeline Data Science Raw Data Media 52

Monte Carlo

AUGUST 6, 2024

Integration Layer : Where your data transformations and business logic are applied. Stage Layer: The Foundation The Stage Layer serves as the foundation of a data warehouse. Its primary purpose is to ingest and store raw data with minimal modifications, preserving the original format and content of incoming data.

Data Warehouse 52

Data Warehouse Raw Data Machine Learning BI 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.

Raw Data 69

Raw Data Data Business Intelligence Data Engineering 69

Wayne Yaddow

FEBRUARY 21, 2025

Complex Data TransformationsTest Planning Best Practices Ensuring data accuracy with structured testing and best practices Photo by Taylor Vick on Unsplash Introduction Data transformations and conversions are crucial for data pipelines, enabling organizations to process, integrate, and refine raw data into meaningful insights.

Data Pipeline 52

Data Pipeline Raw Data Data Data Integration 52

RandomTrees

FEBRUARY 6, 2024

Serving: Delivering Data with Precision: The seamless process significantly enhances the user experience, allowing for intuitive data exploration and decision-making without requiring technical query language knowledge. The significance of GenAI 1.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Lake 52

Cloudera

NOVEMBER 2, 2020

An AdTech company in the US provides processing, payment, and analytics services for digital advertisers. Data processing and analytics drive their entire business. But an important caveat is that ingest speed, semantic richness for developers, data freshness, and query latency are paramount. Data Hub – .

Data Warehouse 97

Data Warehouse Kafka Lambda Architecture Telecommunication 97

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.

Data Lake 52

Data Lake Building Raw Data ETL Tools 52

AltexSoft

MAY 12, 2022

It enables displaying characteristics of audio files, creating all types of audio data visualizations and extracting features from them, to name just a few capabilities. Audio Toolbox by MathWorks offers numerous instruments for audio data processing and analysis, from labeling to estimating signal metrics to extracting certain features.

Machine Learning 122

Machine Learning Building Deep Learning Healthcare 122

DataKitchen

MAY 10, 2024

The Five Use Cases in Data Observability: Mastering Data Production (#3) Introduction Managing the production phase of data analytics is a daunting challenge. Overseeing multi-tool, multi-dataset, and multi-hop data processes ensures high-quality outputs. Have I Checked The Raw Data And The Integrated Data?

Raw Data 50

Raw Data Data Ingestion Datasets Data 50

The Modern Data Company

AUGUST 13, 2024

Introduction to Data Products In today’s data-driven landscape, data products have become essential for maximizing the value of data. As organizations seek to leverage data more effectively, the focus has shifted from temporary datasets to well-defined, reusable data assets.

Raw Data 52

Raw Data Metadata Datasets Utilities 52

AltexSoft

MARCH 14, 2023

As the volume and complexity of data continue to grow, organizations seek faster, more efficient, and cost-effective ways to manage and analyze data. In recent years, cloud-based data warehouses have revolutionized data processing with their advanced massively parallel processing (MPP) capabilities and SQL support.

IT 59

IT Data Warehouse Data Governance Data Lake 59

ProjectPro

DECEMBER 7, 2021

Keeping data in data warehouses or data lakes helps companies centralize the data for several data-driven initiatives. While data warehouses contain transformed data, data lakes contain unfiltered and unorganized raw data.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

Hevo

APRIL 27, 2023

In today’s data-driven era, you have more raw data than ever before. However, to leverage the power of big data, you need to convert raw data into valuable insights for informed decision-making. ” While they may sound […]

Raw Data 52

Raw Data Big Data Data Data Warehouse 52

Databand.ai

AUGUST 30, 2023

Challenges of Legacy Data Architectures Some of the main challenges associated with legacy data architectures include: Lack of flexibility: Traditional data architectures are often rigid and inflexible, making it difficult to adapt to changing business needs and incorporate new data sources or technologies.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 52

Databand.ai

JULY 19, 2023

A Beginner’s Guide [SQ] Niv Sluzki July 19, 2023 ELT is a data processing method that involves extracting data from its source, loading it into a database or data warehouse, and then later transforming it into a format that suits business needs. The extraction process requires careful planning to ensure data integrity.

Data Cleanse 52

Data Cleanse Data Storage Raw Data Data Warehouse 52

Ascend.io

AUGUST 29, 2023

In this article, we’ll break down the intricacies of an end-to-end data pipeline and highlight its importance in today’s landscape. A visual maze: The tangled web of disparate tools commonly used in fragmented data pipelines. Playing the Field – Data Transformation: This is where the action happens.

Data Pipeline 52

Data Pipeline Pipeline-centric Database-centric Data Ingestion 52

Ascend.io

SEPTEMBER 16, 2024

Data pipelines are designed to handle large volumes of data efficiently, ensuring that data moves smoothly through different stages. This process is typically referred to as ETL and the stages include: Extraction: Collecting raw data from multiple sources such as databases, APIs, logs, or files.

Designing 52

Designing Cloud Computing Data Pipeline Raw Data 52

The Modern Data Company

MAY 8, 2023

The Data Lake: A Reservoir of Unstructured Potential A data lake is a centralized repository that stores vast amounts of raw data. It can store any type of data — structured, unstructured, and semi-structured — in its native format, providing a highly scalable and adaptable solution for diverse data needs.

Data Management 52

Data Management Management Data Lake Data Governance 52

The Modern Data Company

MAY 8, 2023

The Data Lake: A Reservoir of Unstructured Potential A data lake is a centralized repository that stores vast amounts of raw data. It can store any type of data — structured, unstructured, and semi-structured — in its native format, providing a highly scalable and adaptable solution for diverse data needs.

Data Management 52

Data Management Management Data Lake Data Governance 52

The Modern Data Company

MAY 8, 2023

The Data Lake: A Reservoir of Unstructured Potential A data lake is a centralized repository that stores vast amounts of raw data. It can store any type of data — structured, unstructured, and semi-structured — in its native format, providing a highly scalable and adaptable solution for diverse data needs.

Data Management 52

Data Management Management Data Lake Data Governance 52

ProjectPro

FEBRUARY 16, 2023

Data integration with ETL has evolved from structured data stores with high computing costs to natural state storage with read operation alterations thanks to the agility of the cloud. Data integration with ETL has changed in the last three decades.

AWS 52

AWS Data Management ETL Tools Management 52

The Modern Data Company

APRIL 15, 2024

Transforming Data Complexity into Strategic Insight At first glance, the process of transforming raw data into actionable insights can seem daunting. The journey from data collection to insight generation often feels like operating a complex machine shrouded in mystery and uncertainty.

Building 52

Building Raw Data Food Data 52

Edureka

JULY 3, 2024

Companies are drowning in a sea of raw data. As data volumes explode across enterprises, the struggle to manage, integrate, and analyze it is getting real. Keep reading to learn in detail about this supremely versatile data integration service created by Microsoft Azure. Now that’s a power couple.

Pipeline-centric 52

Pipeline-centric Data Lake Database-centric Data Pipeline 52

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

Grouparoo

NOVEMBER 29, 2021

The data source is the location of the data that the processing will consume for data processing functions. This can be the point of origin of the data, the place of its creation. Alternatively, this can be data generated by another process and then made available for subsequent processing.

Raw Data 40

Raw Data Big Data Relational Database Data Warehouse 40

Ascend.io

JULY 11, 2024

Metadata is the information that provides context and meaning to data, ensuring it’s easily discoverable, organized, and actionable. It enhances data quality, governance, and automation, transforming raw data into valuable insights. This is what managing data without metadata feels like. Chaos, right?

Metadata 52

Metadata IT Government High Quality Data 52

The Modern Data Company

MAY 10, 2023

To choose the most suitable data management solution for your organization, consider the following factors: Data types and formats: Do you primarily work with structured, unstructured, or semi-structured data? Consider whether you need a solution that supports one or multiple data formats. only structured data).

Data Management 52

Data Management Management Data Lake Data Warehouse 52

Ascend.io

AUGUST 16, 2023

As enterprise usage of data analytics grows, the field has become a significant area of IT expenditure. The collection and preparation of data used for analytics are achieved by building data pipelines that ingest raw data and transform it into useful formats leveraging cloud data platforms like Snowflake, Databricks, and Google BigQuery.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Pipeline 52

AltexSoft

DECEMBER 23, 2022

Integrating data from numerous, disjointed sources and processing it to provide context provides both opportunities and challenges. One of the ways to overcome challenges and gain more opportunities in terms of data integration is to build an ELT (Extract, Load, Transform) pipeline. What is ELT? Scalability.

Process 52

Process Building Raw Data Data Lake 52

Snowflake

APRIL 5, 2023

To keep the data model in good form, Snowflake allows primary key, foreign key, unique key, and “not null” constraints. However, we may verify the entity relationship and data integrity in the data model using Snowflake Tasks. What is dimensional data modeling? This gives a Data Vault the upperhand in this case.

Data Warehouse 52

Data Warehouse Healthcare Unstructured Data Metadata 52

The Modern Data Company

MAY 10, 2023

To choose the most suitable data management solution for your organization, consider the following factors: Data types and formats: Do you primarily work with structured, unstructured, or semi-structured data? Consider whether you need a solution that supports one or multiple data formats. only structured data).

Data Management 52

Data Management Management Data Lake Data Warehouse 52

The Modern Data Company

MAY 10, 2023

To choose the most suitable data management solution for your organization, consider the following factors: Data types and formats: Do you primarily work with structured, unstructured, or semi-structured data? Consider whether you need a solution that supports one or multiple data formats. only structured data).

Data Management 52

Data Management Management Data Lake Data Warehouse 52

Knowledge Hut

JUNE 30, 2023

The practice of designing, building, and maintaining the infrastructure and systems required to collect, process, store, and deliver data to various organizational stakeholders is known as data engineering. You can pace your learning by joining data engineering courses such as the Bootcamp Data Engineer.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Warehouse 52