phData: Data Engineering

APRIL 4, 2023

As organizations continue to leverage data lakes to run analytics and extract insights from their data, progressive marketing intelligence teams are demanding more of them, and solutions like Amazon S3 and automated pipeline support are meeting that demand.

Data Lake 52

Data Lake Amazon Web Services Data Cleanse Data Warehouse 52

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

Data Engineering Data Engineer Engineering Data Lake 52

Knowledge Hut

JANUARY 18, 2024

There are several reasons why it has become a crucial tool: for businesses, it is less expensive and simpler to integrate; for data architects, it is easier to handle multiple data models in the cloud; and for developers, it gives them the ability to generate high-quality analyses and visualizations.

BI 52

BI Data Cleanse Business Intelligence Datasets 52

Knowledge Hut

MAY 3, 2023

Also, data engineers are well-versed in distributed systems, cloud computing, and data modeling. Most data analysts are educated in mathematics, statistics, or a similar subject. Also, data analysts have a thorough comprehension of statistical ideas and methods.

Data Engineering 98

Data Engineering Data Engineer Engineering Data Cleanse 98

Databand.ai

JUNE 28, 2023

Data pipelines often involve a series of stages where data is collected, transformed, and stored. This might include processes like data extraction from different sources, data cleansing, data transformation (like aggregation), and loading the data into a database or a data warehouse.

Data Pipeline 98

Data Pipeline Data Engineering Data Engineer Engineering 98

Towards Data Science

FEBRUARY 6, 2024

Order snapshots are stored in my own development area (image by the author) To prevent my extractions from impacting performance on the operational side, I queried this data regularly and stored it in a persistent staging area (PSA) within my data warehouse.

Systems 80

Systems Raw Data Metadata Data Cleanse 80

ProjectPro

JANUARY 27, 2023

If you're wondering how the ETL process can drive your company to a new era of success, this blog will help you discover what use cases of ETL make it a critical component in many data management and analytic systems. Business Intelligence - ETL is a key component of BI systems for extracting and preparing data for analytics.

BI 52

BI ETL Tools Retail Healthcare 52

Databand.ai

JULY 19, 2023

Enhancing Data Quality Data ingestion plays an instrumental role in enhancing data quality. During the data ingestion process, various validations and checks can be performed to ensure the consistency and accuracy of data. Another way data ingestion enhances data quality is by enabling data transformation.

Data Ingestion 52

Data Ingestion Process Data Cleanse Data Governance 52

Knowledge Hut

OCTOBER 30, 2023

Develop a long-term vision for Power BI implementation and data analytics. Data Architecture and Design: Lead the design and development of complex data architectures, including data warehouses, data lakes, and data marts. Define data architecture standards and best practices.

BI 52

BI Business Intelligence Data Cleanse Business Analyst 52

Knowledge Hut

JANUARY 30, 2024

This data can be structured, semi-structured, or entirely unstructured, making it a versatile tool for collecting information from various origins. The extracted data is then duplicated or transferred to a designated destination, often a data warehouse optimized for Online Analytical Processing (OLAP).

ETL Tools 52

ETL Tools Database-centric Data Mining Data Cleanse 52

RandomTrees

SEPTEMBER 6, 2020

Snowflake Overview A data warehouse is a critical part of any business organization. Lot of cloud-based data warehouses are available in the market today, out of which let us focus on Snowflake. Snowflake is an analytical data warehouse that is provided as Software-as-a-Service (SaaS).

Cloud Storage 52

Cloud Storage Data Ingestion Data Cleanse Data Warehouse 52

Knowledge Hut

NOVEMBER 28, 2023

Technical Data Engineer Skills 1.Python Python Python is one of the most looked upon and popular programming languages, using which data engineers can create integrations, data pipelines, integrations, automation, and data cleansing and analysis.

Data Engineering 52

Data Engineering Data Engineer Engineering Generalist 52

Ascend.io

FEBRUARY 28, 2024

The significance of data engineering in AI becomes evident through several key examples: Enabling Advanced AI Models with Clean Data The first step in enabling AI is the provision of high-quality, structured data.

Data Engineering 52

Data Engineering Data Engineer Engineering Metadata 52

AltexSoft

AUGUST 29, 2023

The term data lake itself is metaphorical, evoking an image of a large body of water fed by multiple streams, each bringing new data to be stored and analyzed. Instead of relying on traditional hierarchical structures and predefined schemas, as in the case of data warehouses, a data lake utilizes a flat architecture.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Databand.ai

JUNE 20, 2023

Niv Sluzki June 20, 2023 What Is Data Integrity? Data integrity refers to the overall accuracy, consistency, and reliability of data stored in a database, data warehouse, or any other information storage system.

Data Integration 52

Data Integration Data Validation Pharmaceutical Data Cleanse 52

Knowledge Hut

JUNE 26, 2023

If you want to break into the field of data engineering but don't yet have any expertise in the field, compiling a portfolio of data engineering projects may help. Data pipeline best practices should be shown in these initiatives. In addition to this, they make sure that the data is always readily accessible to consumers.

Data Engineering 98

Data Engineering Data Engineer Coding Project 98

Knowledge Hut

APRIL 19, 2023

Step 3: Cleanse data: The extracted data is then cleaned to remove inconsistencies, errors, and duplicates from the given dataset Step 4: Combine data: The cleaned data is then combined into a single location, such as a data warehouse or a data lake.

Process 59

Process Data Mining Aggregated Data Portfolio 59

phData: Data Engineering

JULY 18, 2023

With the birth of cloud data warehouses, data applications, and generative AI , processing large volumes of data faster and cheaper is more approachable and desired than ever. First up, let’s dive into the foundation of every Modern Data Stack, a cloud-based data warehouse.

Data Warehouse 52

Data Warehouse Pipeline-centric Government Data 52

AltexSoft

MAY 14, 2021

With the ETL approach, data transformation happens before it gets to a target repository like a data warehouse, whereas ELT makes it possible to transform data after it’s loaded into a target system. Data storage and processing. Data cleansing. Before getting thoroughly analyzed, data ?

Big Data 78

Big Data Data Analytics IT NoSQL 78

AltexSoft

DECEMBER 23, 2022

It is a data integration process with which you first extract raw information (in its original formats) from various sources and load it straight into a central repository such as a cloud data warehouse , a data lake , or a data lakehouse where you transform it into suitable formats for further analysis and reporting.

Process 52

Process Building Raw Data Data Lake 52

Cloudera

JANUARY 11, 2021

NiFi would capture the various datasets, do the required transformations (schema validation, format transformation, data cleansing, etc.) on each dataset and send the datasets in a data warehouse powered by Hive. Once the data is sent there, NiFi could trigger a Hive query to perform the joint operation.

Kafka 61

Kafka Java Data Cleanse Datasets 61

ProjectPro

AUGUST 24, 2021

Data Warehousing: Data warehousing utilizes and builds a warehouse for storing data. A data engineer interacts with this warehouse almost on an everyday basis. Data Analytics: A data engineer works with different teams who will leverage that data for business solutions.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

phData: Data Engineering

AUGUST 4, 2023

Once the data is loaded into Snowflake, it can be further processed and transformed using SQL queries or other tools within the Snowflake environment. This includes tasks such as data cleansing, enrichment, and aggregation.

Cloud Storage 52

Cloud Storage Google Cloud Amazon Web Services Data Storage 52

Netflix Tech

MARCH 25, 2019

We are designing a CRUD layer and exposing it as REST APIs to make it easier for anyone to publish lineage data to our pipelines. We are tackling a bunch of very interesting known unknowns with exciting initiatives in the field of data catalog and asset inventory.

Building 87

Building Metadata Transportation Data Ingestion 87

Knowledge Hut

OCTOBER 13, 2023

It improves performance by minimizing data transferred to Power BI for processing. Transform Data in Source: Whenever possible, perform data transformations in the source database or data warehouse before loading the data into Power BI.

BI 52

BI Business Analyst Datasets Raw Data 52

DataKitchen

JUNE 24, 2021

Previously we would have a very laborious data warehouse or data mart initiative and it may take a very long time and have a large price tag. We actually broke down that process and began to understand that the data cleansing and gathering upfront often contributed several months of cycle time to the process.

Pipeline-centric 84

Pipeline-centric Manufacturing Education Data Cleanse 84

Rockset

SEPTEMBER 6, 2019

There are several different options for storing and analyzing data, but our use case has very specific requirements: real-time, low-latency analytics with fast queries on data without a fixed schema, using SQL as the query language. Most analytics engines require the data to be formatted and structured in a specific schema.

Metadata 40

Metadata Kafka Data Cleanse SQL 40

ProjectPro

JANUARY 31, 2023

This process involves learning to understand the data and determining what needs to be done before the data becomes useful in a specific context. Discovery is a big task that may be performed with the help of data visualization tools that help consumers browse their data.

Big Data 52

Big Data Hadoop AWS Relational Database 52

ProjectPro

OCTOBER 20, 2021

Having multiple data integration routes helps optimize the operational as well as analytical use of data. Experimentation in production Big Data Data Warehouse for core ETL tasks Direct data pipelines Tiered Data Lake 4.

Machine Learning 52

Machine Learning Algorithm Government Data Science 52