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?

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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.

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Data Pipeline Data Science Raw Data Media 52

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.

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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

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?

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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.

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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.

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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

ProjectPro

FEBRUARY 8, 2023

Do ETL and data integration activities seem complex to you? Read this blog to understand everything about AWS Glue that makes it one of the most popular data integration solutions in the industry. Did you know the global big data market will likely reach $268.4 Businesses are leveraging big data now more than ever.

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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.

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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

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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

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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.

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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!

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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?

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Metadata IT Government High Quality Data 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)?

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Databand.ai

AUGUST 30, 2023

Unified DataOps represents a fresh approach to managing and synchronizing data operations across several domains, including data engineering, data science, DevOps, and analytics. The goal of this strategy is to streamline the entire process of extracting insights from raw data by removing silos between teams and technologies.

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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 Data Lake Management 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.

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Data Management Management Data Lake Data Governance 52

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.

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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.

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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.

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Data Engineer Data Engineering Engineering Data Pipeline 52

Monte Carlo

MAY 28, 2024

This involves connecting to multiple data sources, using extract, transform, load ( ETL ) processes to standardize the data, and using orchestration tools to manage the flow of data so that it’s continuously and reliably imported – and readily available for analysis and decision-making.

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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.

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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).

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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.

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Data Engineer Data Engineering Engineering 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

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

Monte Carlo

AUGUST 25, 2023

Data lakes also typically decouple storage and comput e, which can enable cost savings while facilitating real-time streaming and querying. They also encourage distributed computation for enhanced query performance and parallel data processing. A data lakehouse attempts to get the best of both worlds.

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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.

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Data Warehouse Healthcare Unstructured Data Metadata 52

Ascend.io

JULY 28, 2023

Now, you might ask, “How is this different from data stack architecture, or data architecture?” ” Data Stack Architecture : Your data stack architecture defines the technology and tools used to handle data, like databases, data processing platforms, analytic tools, and programming languages.

Data Pipeline 52

Data Pipeline Architecture Lambda Architecture Data Architecture 52

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 101

Banking Raw Data High Quality Data Business Intelligence 101

Knowledge Hut

JUNE 28, 2023

Data Sources Diverse and vast data sources, including structured, unstructured, and semi-structured data. Structured data from databases, data warehouses, and operational systems. Goal Extracting valuable information from raw data for predictive or descriptive purposes.

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Data Mining Business Intelligence BI Structured Data 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.

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Process Building Raw Data Data Lake 52

Ascend.io

SEPTEMBER 14, 2023

Integration with Spark: When paired with platforms like Spark, Python’s performance is further amplified. PySpark, for instance, optimizes distributed data operations across clusters, ensuring faster data processing. Use Case: Processing streaming tweets from pyspark.streaming import StreamingContext from pyspark.

Data Engineer 52

Data Engineer Data Engineering Python Engineering 52

Knowledge Hut

DECEMBER 28, 2023

An Azure Data Engineer is responsible for designing, implementing and managing data solutions on Microsoft Azure. The Azure Data Engineer certification imparts to them a deep understanding of data processing, storage and architecture. Knowledge of data integration patterns and techniques using Azure services.

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