Data Science Dojo

JANUARY 12, 2023

When it comes to data, there are two main types: data lakes and data warehouses. What is a data lake? An enormous amount of raw data is stored in its original format in a data lake until it is required for analytics applications. Which one is right for your business?

Data Lakes 221

Data Lakes Data Warehouse Hadoop Machine Learning 221

phData

APRIL 29, 2024

Recognizing these specific needs, Fivetran has developed a range of connectors, including dedicated applications, databases, files, and events, which can accommodate the diverse formats used by healthcare systems. Addressing these needs may pose challenges that lead to the implementation of custom solutions rather than a uniform approach.

SQL 52

SQL Azure Data Warehouse Cloud Data 52

AWS Machine Learning Blog

JUNE 13, 2023

The natural language capabilities allow non-technical users to query data through conversational English rather than complex SQL. The AI and language models must identify the appropriate data sources, generate effective SQL queries, and produce coherent responses with embedded results at scale.

Database 72

Database SQL AWS AI 72

Smart Data Collective

FEBRUARY 23, 2022

The size and the variety of data that enterprises have to deal with have become more complex and larger. Traditional relational databases provide certain benefits, but they are not suitable to handle big and various data. AWS Athena is a query service that allows users to analyze data in S3 using standard SQL syntax.

Data Lakes 99

Data Lakes AWS SQL Database 99

phData

MARCH 22, 2024

Snowflake-managed Iceberg table’s performance is at par with Snowflake native tables while storing the data in public cloud storage. They are Ideal for situations where the data is already stored in data lakes and do not intend to load into Snowflake but need to use the features and performance of Snowflake.

SQL 52

SQL AWS Database Data Lakes 52

Dataversity

MARCH 12, 2021

blog series, we experiment with the most interesting blends of data and tools. Whether it’s mixing traditional sources with modern data lakes, open-source devops on the cloud with protected internal legacy tools, SQL with noSQL, web-wisdom-of-the-crowd with in-house handwritten notes, or IoT […].

Data Lakes 52

Data Lakes SQL Database Data Science 52

phData

SEPTEMBER 26, 2023

Sources The sources involved could influence or determine the options available for the data ingestion tool(s). These could include other databases, data lakes, SaaS applications (e.g. Salesforce), Access databases, SharePoint, or Excel spreadsheets. Data flows from the current data platform to the destination.

SQL 52

SQL Database ETL Data Modeling 52

Dataversity

MAY 7, 2021

blog series, we experiment with the most interesting blends of data and tools. Whether it’s mixing traditional sources with modern data lakes, open-source DevOps on the cloud with protected internal legacy tools, SQL with noSQL, web-wisdom-of-the-crowd with in-house handwritten notes, or IoT […].

Data Lakes 52

Data Lakes SQL Database Data Science 52

SAS Software

NOVEMBER 29, 2023

Azure Data Lake Storage (ADLS) Gen2のストレージアカウントの作成 3-2.ストレージアカウントのデータストレージコンテナの作成 Azure SynapseのSQLデータベースをSASライブラリとして定義 4-3.Azure Bulkload機能について 3.BULKLOAD機能を利用するためのAzure側で必要なサービスの作成 BULKLOAD機能を利用するためのAzure側で必要なサービスの作成 3-1.Azure ストレージアカウントのデータストレージコンテナの作成 3-3.ストレージアカウントの利用ユーザー権限の設定 ストレージアカウントの利用ユーザー権限の設定 3-4.データ書き込み用のSASコードの実行

Azure 59

Azure Data Lakes SQL Analytics 59

Pickl AI

NOVEMBER 15, 2023

Discover the nuanced dissimilarities between Data Lakes and Data Warehouses. Data management in the digital age has become a crucial aspect of businesses, and two prominent concepts in this realm are Data Lakes and Data Warehouses. It acts as a repository for storing all the data.

Data Lakes 52

Data Lakes Data Warehouse Database ETL 52

The MLOps Blog

JANUARY 23, 2023

However, there are some key differences that we need to consider: Size and complexity of the data In machine learning, we are often working with much larger data. Basically, every machine learning project needs data. First of all, machine learning engineers and data scientists often use data from different data vendors.

ML 52

ML ML Data Lakes Machine Learning 52

Data Science 101

NOVEMBER 7, 2019

Azure Synapse Analytics can be seen as a merge of Azure SQL Data Warehouse and Azure Data Lake. Synapse allows one to use SQL to query petabytes of data, both relational and non-relational, with amazing speed. Azure Synapse. I think this announcement will have a very large and immediate impact.

Data Science 106

Data Science Azure SQL Machine Learning 106

Pickl AI

APRIL 6, 2023

Sound knowledge of relational databases or NoSQL databases like Cassandra. Data Warehousing concepts and knowledge should be strong. Having experience using at least one end-to-end Azure data lake project. Strong skills in working with Azure cloud-based environment with delta lake implementation.

Azure 52

Azure Data Engineering Data Engineer Data Engineering 52

Pickl AI

JULY 6, 2023

Hive is a data warehousing infrastructure built on top of Hadoop. It has the following features: It facilitates querying, summarizing, and analyzing large datasets Hadoop also provides a SQL-like language called HiveQL Hive allows users to write queries to extract valuable insights from structured and semi-structured data stored in Hadoop.

Hadoop 52

Hadoop SQL Big Data Big Data 52

ODSC - Open Data Science

APRIL 3, 2023

Skills like effective verbal and written communication will help back up the numbers, while data visualization (specific frameworks in the next section) can help you tell a complete story. Data Wrangling: Data Quality, ETL, Databases, Big Data The modern data analyst is expected to be able to source and retrieve their own data for analysis.

Analytics 52

Analytics Analytics Data Analyst SQL 52

AWS Machine Learning Blog

FEBRUARY 28, 2024

Structured Query Language (SQL) is a complex language that requires an understanding of databases and metadata. Today, generative AI can enable people without SQL knowledge. The solution in this post aims to bring enterprise analytics operations to the next level by shortening the path to your data using natural language.

SQL 114

SQL AWS Database ML 114

ODSC - Open Data Science

JUNE 12, 2023

They all agree that a Datamart is a subject-oriented subset of a data warehouse focusing on a particular business unit, department, subject area, or business functionality. The Datamart’s data is usually stored in databases containing a moving frame required for data analysis, not the full history of data.

Power BI 52

Power BI Data Warehouse ETL Data Preparation 52

phData

NOVEMBER 6, 2023

Example: models: my_project: events: # materialize all models in models/events as tables +materialized: table csvs: # this is redundant, and does not need to be set +materialized: view We can also configure the materialization type inside the dbt SQL file or the yaml file. Ephemeral Ephemeral models are not a permanent part of the database.

Clustering 52

Clustering SQL Python Database 52

IBM Journey to AI blog

JANUARY 5, 2023

The first generation of data architectures represented by enterprise data warehouse and business intelligence platforms were characterized by thousands of ETL jobs, tables, and reports that only a small group of specialized data engineers understood, resulting in an under-realized positive impact on the business.

Data Quality 52

Data Quality Data Lakes Data Warehouse Business Intelligence 52

Mlearning.ai

FEBRUARY 16, 2023

Thus, the solution allows for scaling data workloads independently from one another and seamlessly handling data warehousing, data lakes , data sharing, and engineering. Snowflake Database Pros Extensive Storage Opportunities Snowflake provides affordability, scalability, and a user-friendly interface.

Data Warehouse 52

Data Warehouse Business Intelligence Business Intelligence Database 52

IBM Journey to AI blog

SEPTEMBER 19, 2023

And you should have experience working with big data platforms such as Hadoop or Apache Spark. Additionally, data science requires experience in SQL database coding and an ability to work with unstructured data of various types, such as video, audio, pictures and text.

Data Science 86

Data Science Analytics Analytics Data Scientist 86

Pickl AI

AUGUST 1, 2023

The primary goal of Data Engineering is to transform raw data into a structured and usable format that can be easily accessed, analyzed, and interpreted by Data Scientists, analysts, and other stakeholders. Future of Data Engineering The Data Engineering market will expand from $18.2

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Flipboard

JUNE 26, 2023

Companies are faced with the daunting task of ingesting all this data, cleansing it, and using it to provide outstanding customer experience. Typically, companies ingest data from multiple sources into their data lake to derive valuable insights from the data. For Database , choose c360_workshop_db.

AWS 92

AWS ML ML ETL 92

phData

FEBRUARY 14, 2023

Data integration is essentially the Extract and Load portion of the Extract, Load, and Transform (ELT) process. Data ingestion involves connecting your data sources, including databases, flat files, streaming data, etc, to your data warehouse. Snowflake provides native ways for data ingestion.

Data Warehouse 59

Data Warehouse Azure AWS Database 59

phData

JULY 10, 2023

An external table is a Snowflake feature that lives outside of a database in a text-based, delimited file or in a fixed-length format file. It can be used to store data outside the database while retaining the ability to query its data. The FILE_FORMAT parameter is set to CSV since the data file is in CSV format.

Data Lakes 40

Data Lakes Azure Database AWS 40

phData

AUGUST 9, 2023

This makes ELT aligned with modern data practices and helps explain why it has become the dominant pattern, replacing the once-standard ETL approach. The Story of ELT In the early days of data warehousing, ETL was the standard for data processing. ETL systems just couldn’t handle the massive flows of raw data.

ETL 52

ETL Data Warehouse Cloud Data Big Data 52

IBM Journey to AI blog

MARCH 14, 2024

The existence of data silos and duplication, alongside apprehensions regarding data quality, presents a multifaceted environment for organizations to manage. Also, traditional database management tasks, including backups, upgrades and routine maintenance drain valuable time and resources, hindering innovation.

AWS 85

AWS Database AI AI 85

Tableau

MARCH 30, 2021

we’ve added new connectors to help our customers access more data in Azure than ever before: an Azure SQL Database connector and an Azure Data Lake Storage Gen2 connector. As our customers increasingly adopt the cloud, we continue to make investments that ensure they can access their data anywhere.

Azure 99

Azure Tableau Data Lakes SQL 99

Flipboard

DECEMBER 6, 2023

In this post, we discuss a Q&A bot use case that Q4 has implemented, the challenges that numerical and structured datasets presented, and how Q4 concluded that using SQL may be a viable solution. RAG with semantic search – Conventional RAG with semantic search was the last step before moving to SQL generation.

SQL 130

SQL Database AWS Machine Learning 130

AWS Machine Learning Blog

JANUARY 4, 2024

One such area that is evolving is using natural language processing (NLP) to unlock new opportunities for accessing data through intuitive SQL queries. Instead of dealing with complex technical code, business users and data analysts can ask questions related to data and insights in plain language.

SQL 122

SQL Database AI AI 122

Analytics Vidhya

MARCH 21, 2023

Introduction In today’s world, data is growing exponentially with time with digitalization. to store and analyze this data to get valuable business insights from it. Organizations are using various cloud platforms like Azure, GCP, etc.,

Azure 274

Azure Analytics Analytics Data Lakes 274

DagsHub

DECEMBER 11, 2023

Adding new data to the storage requires pulling the existing data, then calculating the new hash before pushing back the whole data. DVC lacks crucial relational database features, making it an unsuitable choice for those familiar with relational databases. So, Dolt’s integration with Git makes it easier to learn.

Machine Learning 98

Machine Learning Machine Learning Data Lakes Database 98

ODSC - Open Data Science

JANUARY 18, 2024

So as you take inventory of your existing skill set, you’ll want to start to identify the areas where you need to focus on to become a data engineer. These areas may include SQL, database design, data warehousing, distributed systems, cloud platforms (AWS, Azure, GCP), and data pipelines.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

The MLOps Blog

MARCH 15, 2023

Data Processing : You need to save the processed data through computations such as aggregation, filtering and sorting. Data Storage : To store this processed data to retrieve it over time – be it a data warehouse or a data lake. Relational database connectors are available.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59

Tableau

MARCH 29, 2021

we’ve added new connectors to help our customers access more data in Azure than ever before: an Azure SQL Database connector and an Azure Data Lake Storage Gen2 connector. As our customers increasingly adopt the cloud, we continue to make investments that ensure they can access their data anywhere.

Azure 52

Azure Tableau Data Lakes SQL 52

Alation

NOVEMBER 28, 2022

But refreshing this analysis with the latest data was impossible… unless you were proficient in SQL or Python. We wanted to make it easy for anyone to pull data and self service without the technical know-how of the underlying database or data lake.

Database 52

Database Data Governance Data Quality Data Lakes 52

phData

SEPTEMBER 28, 2023

Dataflows represent a cloud-based technology designed for data preparation and transformation purposes. Dataflows have different connectors to retrieve data, including databases, Excel files, APIs, and other similar sources, along with data manipulations that are performed using Online Power Query Editor.

Power BI 78

Power BI Data Preparation Machine Learning Machine Learning 78