Pickl AI

JUNE 7, 2024

Summary: Choosing the right ETL tool is crucial for seamless data integration. Top contenders like Apache Airflow and AWS Glue offer unique features, empowering businesses with efficient workflows, high data quality, and informed decision-making capabilities. Choosing the right ETL tool is crucial for smooth data management.

ETL 40

ETL Data Quality Data Pipeline Data Warehouse 40

phData

JULY 10, 2024

Sigma Computing , a cloud-based analytics platform, helps data analysts and business professionals maximize their data with collaborative and scalable analytics. One of Sigma’s key features is its support for custom SQL queries and CSV file uploads. These tools allow users to handle more advanced data tasks and analyses.

SQL 52

SQL Data Warehouse Analytics Analytics 52

AWS Machine Learning Blog

FEBRUARY 21, 2025

Data exploration and model development were conducted using well-known machine learning (ML) tools such as Jupyter or Apache Zeppelin notebooks. Apache Hive was used to provide a tabular interface to data stored in HDFS, and to integrate with Apache Spark SQL. HBase is employed to offer real-time key-based access to data.

Data Science 72

Data Science AWS Hadoop Data Scientist 72

phData

OCTOBER 9, 2023

In this blog, we explore best practices and techniques to optimize Snowflake’s performance for data vault modeling , enabling your organizations to achieve efficient data processing, accelerated query performance, and streamlined ETL workflows. This can make it nearly impossible to “handwrite” these SQL queries.

ETL 69

ETL Clustering Data Warehouse SQL 69

phData

SEPTEMBER 19, 2023

However, to fully harness the potential of a data lake, effective data modeling methodologies and processes are crucial. Data modeling plays a pivotal role in defining the structure, relationships, and semantics of data within a data lake. Consistency of data throughout the data lake.

Data Lakes 52

Data Lakes Data Modeling Data Models Data Warehouse 52

phData

SEPTEMBER 27, 2024

Introduction: The Customer Data Modeling Dilemma You know, that thing we’ve been doing for years, trying to capture the essence of our customers in neat little profile boxes? For years, we’ve been obsessed with creating these grand, top-down customer data models. Yeah, that one.

Data Models 52

Data Models Data Modeling Apache Kafka Data Lakes 52

ODSC - Open Data Science

JUNE 12, 2023

This article is an excerpt from the book Expert Data Modeling with Power BI, Third Edition by Soheil Bakhshi, a completely updated and revised edition of the bestselling guide to Power BI and data modeling. Then we have some other ETL processes to constantly land the past 5 years of data into the Datamarts.

Power BI 52

Power BI Data Warehouse ETL Data Preparation 52

Women in Big Data

NOVEMBER 27, 2024

By maintaining historical data from disparate locations, a data warehouse creates a foundation for trend analysis and strategic decision-making. Evaluate integration capabilities with existing data sources and Extract Transform and Load (ETL) tools. Its PostgreSQL foundation ensures compatibility with most SQL clients.

Data Warehouse 59

Data Warehouse Big Data Big Data Azure 59

Pickl AI

JULY 3, 2023

It is the process of converting raw data into relevant and practical knowledge to help evaluate the performance of businesses, discover trends, and make well-informed choices. Data gathering, data integration, data modelling, analysis of information, and data visualization are all part of intelligence for businesses.

Business Intelligence 52

Business Intelligence Business Intelligence SQL Data Visualization 52

phData

SEPTEMBER 26, 2023

Data flows from the current data platform to the destination. The necessary access is granted so data flows without issue. SQL Server Agent jobs). Transformations Transformations can be a part of data ingestion (ETL pattern) or can take place at a later stage after data has been landed (ELT pattern).

SQL 52

SQL Database ETL Data Modeling 52

Pickl AI

MARCH 3, 2025

Summary: Business Intelligence Analysts transform raw data into actionable insights. They use tools and techniques to analyse data, create reports, and support strategic decisions. Key skills include SQL, data visualization, and business acumen. Introduction We are living in an era defined by data.

Business Intelligence 52

Business Intelligence Business Intelligence Data Analyst Data Visualization 52

Pickl AI

JULY 25, 2023

Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. Data Visualization: Matplotlib, Seaborn, Tableau, etc.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Pickl AI

NOVEMBER 4, 2024

Summary: The fundamentals of Data Engineering encompass essential practices like data modelling, warehousing, pipelines, and integration. Understanding these concepts enables professionals to build robust systems that facilitate effective data management and insightful analysis. What is Data Engineering?

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

ODSC - Open Data Science

SEPTEMBER 27, 2023

In contrast, data warehouses and relational databases adhere to the ‘Schema-on-Write’ model, where data must be structured and conform to predefined schemas before being loaded into the database. Schema Enforcement: Data warehouses use a “schema-on-write” approach. This ensures data consistency and integrity.

Data Lakes 98

Data Lakes Data Warehouse Database Big Data 98

phData

AUGUST 10, 2023

That said, dbt provides the ability to generate data vault models and also allows you to write your data transformations using SQL and code-reusable macros powered by Jinja2 to run your data pipelines in a clean and efficient way. The most important reason for using DBT in Data Vault 2.0

SQL 52

SQL Data Observability Data Quality Data Pipeline 52

Pickl AI

JANUARY 28, 2025

Data Integration Once data is collected from various sources, it needs to be integrated into a cohesive format. Data Quality Management : Ensures that the integrated data is accurate, consistent, and reliable for analysis. They are useful for big data analytics where flexibility is needed.

Business Intelligence 52

Business Intelligence Business Intelligence ETL Data Lakes 52

Pickl AI

APRIL 6, 2023

Understand the fundamentals of data engineering: To become an Azure Data Engineer, you must first understand the concepts and principles of data engineering. Knowledge of data modeling, warehousing, integration, pipelines, and transformation is required. Hands-on experience working with SQLDW and SQL-DB.

Azure 52

Azure Data Engineering Data Engineer Data Engineering 52

Pickl AI

AUGUST 9, 2024

Hierarchies align data modelling with business processes, making it easier to analyse data in a context that reflects real-world operations. Designing Hierarchies Designing effective hierarchies requires careful consideration of the business requirements and the data model.

Data Warehouse 52

Data Warehouse Data Quality ETL Business Intelligence 52

phData

JULY 11, 2023

Power BI Datamarts provides a low/no code experience directly within Power BI Service that allows developers to ingest data from disparate sources, perform ETL tasks with Power Query, and load data into a fully managed Azure SQL database. Blog: Data Modeling Fundamentals in Power BI. a.

Power BI 40

Power BI SQL Azure ETL 40

phData

JUNE 27, 2023

Using SQL-centric transformations to model data to be deployed. dbt is also great for data lineage and documentation to empower business analysts to make informed decisions on their data. Is dbt an Ideal Fit for YOUR Organization’s Data Stack? It is a compiler and a runner. Proceed as you see fit.

SQL 52

SQL Database Data Quality Data Warehouse 52

phData

JULY 17, 2023

In order to fully leverage this vast quantity of collected data, companies need a robust and scalable data infrastructure to manage it. This is where Fivetran and the Modern Data Stack come in. Snowflake Data Cloud Replication Transferring data from a source system to a cloud data warehouse.

Data Warehouse 52

Data Warehouse Data Pipeline Cloud Data ETL 52

The MLOps Blog

MARCH 15, 2023

If you will ask data professionals about what is the most challenging part of their day to day work, you will likely discover their concerns around managing different aspects of data before they get to graduate to the data modeling stage. Uses secure protocols for data security.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59

Dataconomy

MARCH 24, 2023

Some of the common career opportunities in BI include: Entry-level roles Data analyst:  A data analyst is responsible for collecting and analyzing data, creating reports, and presenting insights to stakeholders. They may also be involved in data modeling and database design.

Business Intelligence 70

Business Intelligence Business Intelligence Data Analysis Data Analysis 70

Dataconomy

MARCH 24, 2023

Some of the common career opportunities in BI include: Entry-level roles Data analyst:  A data analyst is responsible for collecting and analyzing data, creating reports, and presenting insights to stakeholders. They may also be involved in data modeling and database design.

Business Intelligence 70

Business Intelligence Business Intelligence Data Analysis Data Analysis 70

phData

OCTOBER 17, 2024

Apache Airflow Airflow is an open-source ETL software that is very useful when paired with Snowflake. dbt offers a SQL-first transformation workflow that lets teams build data transformation pipelines while following software engineering best practices like CI/CD, modularity, and documentation.

AI 52

AI AI SQL Data Quality 52

Alation

OCTOBER 27, 2022

We document these custom models in Alation Data Catalog and publish common queries that other teams can use for operational use cases or reporting needs. Contact title mappings, which are buiilt in some of data models, are documented within our data catalog. Jason: How do you use these models?

Data Analyst 52

Data Analyst Data Scientist Analytics Analytics 52

IBM Journey to AI blog

JANUARY 5, 2023

The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases. Perform data quality monitoring based on pre-configured rules.

Data Quality 52

Data Quality Data Lakes Data Warehouse Big Data 52

AWS Machine Learning Blog

AUGUST 21, 2023

Data professionals such as data scientists want to use the power of Apache Spark , Hive , and Presto running on Amazon EMR for fast data preparation; however, the learning curve is steep. Solution overview We demonstrate this solution with an end-to-end use case using a sample dataset, the TPC data model.

AWS 97

AWS Data Lakes Clustering Data Preparation 97

DataSeries

AUGUST 15, 2024

Knowledge of Core Data Engineering Concepts Ensure one possess a strong foundation in core data engineering concepts, which include data structures, algorithms, database management systems, data modeling , data warehousing , ETL (Extract, Transform, Load) processes, and distributed computing frameworks (e.g.,

Data Engineering 40

Data Engineering Data Engineer Data Engineering Data Engineering 40

phData

SEPTEMBER 28, 2023

Power BI Dataflows provide vital functionalities that effectively empower users to cleanse and reshape data from various sources. These Dataflows are crucial in fostering consistency and reducing the duplication of repetitive ETL (Extract, Transform, Load) steps, achieved by reusing transformations.

Power BI 78

Power BI Data Preparation Machine Learning Machine Learning 78

Pickl AI

AUGUST 11, 2024

These tools enable effective data structuring, transformation, and analysis, supporting best practices for dimensional modelling and ensuring high-quality, consistent business metrics. These tools help streamline the design process and ensure consistency.

Data Quality 52

Data Quality Data Warehouse Data Governance Analytics 52

DagsHub

OCTOBER 23, 2024

Here’s the structured equivalent of this same data in tabular form: With structured data, you can use query languages like SQL to extract and interpret information. In contrast, such traditional query languages struggle to interpret unstructured data. is similar to the traditional Extract, Transform, Load (ETL) process.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

Pickl AI

JULY 18, 2024

Comparison with Traditional Relational Databases Traditional relational databases (RDBMS) like MySQL or PostgreSQL store data in structured tables with predefined schemas. In contrast, MongoDB’s document-based model allows for a more flexible and scalable approach. MongoDB is a NoSQL database that uses a document-oriented data model.

Database 52

Database SQL Data Analyst Database Administration 52

AWS Machine Learning Blog

SEPTEMBER 18, 2024

An example direct acyclic graph (DAG) might automate data ingestion, processing, model training, and deployment tasks, ensuring that each step is run in the correct order and at the right time. Though it’s worth mentioning that Airflow isn’t used at runtime as is usual for extract, transform, and load (ETL) tasks.

AWS 118

AWS Machine Learning Machine Learning ML 118

phData

APRIL 20, 2023

Variant columns can be used to store data that doesn’t fit neatly into traditional columns, such as nested data structures, arrays, or key-value pairs. Using variant columns in data vault satellites in Snowflake can provide several benefits. If data is present, Tasks runs SQL to push it to the raw data vault objects.

Data Warehouse 72

Data Warehouse Data Governance Clustering Database 72

phData

JULY 18, 2023

Slow Response to New Information: Legacy data systems often lack the computation power necessary to run efficiently and can be cost-inefficient to scale. This typically results in long-running ETL pipelines that cause decisions to be made on stale or old data.

Data Warehouse 52

Data Warehouse Analytics Analytics SQL 52

Pickl AI

MARCH 19, 2025

Summary: Data engineering tools streamline data collection, storage, and processing. Tools like Python, SQL, Apache Spark, and Snowflake help engineers automate workflows and improve efficiency. Learning these tools is crucial for building scalable data pipelines.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52