phData

SEPTEMBER 19, 2023

With the amount of data companies are using growing to unprecedented levels, organizations are grappling with the challenge of efficiently managing and deriving insights from these vast volumes of structured and unstructured data. What is a Data Lake? Consistency of data throughout the data lake.

Data Lakes 52

Data Lakes Data Models Data Modeling Data Warehouse 52

Pickl AI

APRIL 6, 2023

Accordingly, one of the most demanding roles is that of Azure Data Engineer Jobs that you might be interested in. The following blog will help you know about the Azure Data Engineering Job Description, salary, and certification course. How to Become an Azure Data Engineer?

Azure 52

Azure Data Engineering Data Engineering Data Engineer 52

AWS Machine Learning Blog

JUNE 20, 2024

Our goal was to improve the user experience of an existing application used to explore the counters and insights data. The data is stored in a data lake and retrieved by SQL using Amazon Athena. The following figure shows a search query that was translated to SQL and run. The challenge is to assure quality.

SQL 106

SQL Database AWS Machine Learning 106

ODSC - Open Data Science

JANUARY 18, 2024

Data engineering is a rapidly growing field, and there is a high demand for skilled data engineers. If you are a data scientist, you may be wondering if you can transition into data engineering. In this blog post, we will discuss how you can become a data engineer if you are a data scientist.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

Pickl AI

AUGUST 1, 2023

Aspiring and experienced Data Engineers alike can benefit from a curated list of books covering essential concepts and practical techniques. These 10 Best Data Engineering Books for beginners encompass a range of topics, from foundational principles to advanced data processing methods. What is Data Engineering?

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

Data Science Blog

MAY 15, 2023

tl;dr Ein Data Lakehouse ist eine moderne Datenarchitektur, die die Vorteile eines Data Lake und eines Data Warehouse kombiniert. Die Definition eines Data Lakehouse Ein Data Lakehouse ist eine moderne Datenspeicher- und -verarbeitungsarchitektur, die die Vorteile von Data Lakes und Data Warehouses vereint.

Data Warehouse 147

Data Warehouse Data Lakes Azure AWS 147

Data Science Dojo

SEPTEMBER 11, 2024

With this full-fledged solution, you don’t have to spend all your time and effort combining different services or duplicating data. Overview of One Lake Fabric features a lake-centric architecture, with a central repository known as OneLake. On the home page, select Synapse Data Engineering.

Power BI 195

Power BI Data Pipeline Data Warehouse Data Engineering 195

phData

NOVEMBER 8, 2024

Data Versioning and Time Travel Open Table Formats empower users with time travel capabilities, allowing them to access previous dataset versions. Note : Cloud Data warehouses like Snowflake and Big Query already have a default time travel feature. It can also be integrated into major data platforms like Snowflake.

Data Lakes 52

Data Lakes Data Warehouse Database Azure 52

Dataconomy

JUNE 1, 2023

Unified data storage : Fabric’s centralized data lake, Microsoft OneLake, eliminates data silos and provides a unified storage system, simplifying data access and retrieval. OneLake is designed to store a single copy of data in a unified location, leveraging the open-source Apache Parquet format.

Power BI 194

Power BI Data Lakes Azure Data Silos 194

Snorkel AI

MAY 26, 2023

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Welcome everybody.

SQL 52

SQL ML ML Python 52

Snorkel AI

MAY 26, 2023

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Welcome everybody.

SQL 52

SQL ML ML Python 52

Smart Data Collective

FEBRUARY 23, 2022

Traditional relational databases provide certain benefits, but they are not suitable to handle big and various data. That is when data lake products started gaining popularity, and since then, more companies introduced lake solutions as part of their data infrastructure. Athena is serverless and managed by AWS.

Data Lakes 103

Data Lakes AWS SQL Big Data 103

ODSC - Open Data Science

FEBRUARY 2, 2023

Computer Science and Computer Engineering Similar to knowing statistics and math, a data scientist should know the fundamentals of computer science as well. While knowing Python, R, and SQL are expected, you’ll need to go beyond that. Big Data As datasets become larger and more complex, knowing how to work with them will be key.

Data Science 98

Data Science Data Scientist Computer Science Computer Science 98

AWS Machine Learning Blog

JUNE 5, 2023

Many teams are turning to Athena to enable interactive querying and analyze their data in the respective data stores without creating multiple data copies. Athena allows applications to use standard SQL to query massive amounts of data on an S3 data lake. Create a data lake with Lake Formation.

Machine Learning 84

Machine Learning Machine Learning AWS Data Lakes 84

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. Given the range of tools and data types, a separate data versioning logic will be necessary.

ML 52

ML ML Data Lakes Machine Learning 52

ODSC - Open Data Science

APRIL 3, 2023

Data analysts often must go out and find their data, process it, clean it, and get it ready for analysis. This pushes into Big Data as well, as many companies now have significant amounts of data and large data lakes that need analyzing. Cloud Services: Google Cloud Platform, AWS, Azure.

Analytics 52

Analytics Analytics Data Analyst Data Science 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. This text has a lot of information, but it is not structured.

Machine Learning 52

Machine Learning Machine Learning AI AI 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

ODSC - Open Data Science

MARCH 30, 2023

5 Reasons Why SQL is Still the Most Accessible Language for New Data Scientists Between its ability to perform data analysis and ease-of-use, here are 5 reasons why SQL is still ideal for new data scientists to get into the field. Check a few of them out here.

Azure 52

Azure ML ML Data Models 52

ODSC - Open Data Science

APRIL 24, 2023

To cluster the data we have to calculate distances between IPs — The number of all possible IP pairs is very large, and we had to solve the scale problem. Data Processing and Clustering Our data is stored in a Data Lake and we used PrestoDB as a query engine. AS ip_1, r.ip AND l.ip < r.ip

Clustering 52

Clustering SQL Algorithm ML 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 98

Data Science Analytics Analytics Data Scientist 98

Pickl AI

SEPTEMBER 5, 2024

Storage Solutions: Secure and scalable storage options like Azure Blob Storage and Azure Data Lake Storage. Key features and benefits of Azure for Data Science include: Scalability: Easily scale resources up or down based on demand, ideal for handling large datasets and complex computations.

Azure 52

Azure Data Scientist Machine Learning Machine Learning 52

Analytics Vidhya

OCTOBER 6, 2023

In today’s rapidly evolving digital landscape, seamless data, applications, and device integration are more pressing than ever. Enter Microsoft Fabric, a cutting-edge solution designed to revolutionize how we interact with technology.

Analytics 334

Analytics Analytics Power BI Data Lakes 334

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. Data warehousing is a vital constituent of any business intelligence operation.

Data Warehouse 52

Data Warehouse Business Intelligence Business Intelligence Database 52

phData

FEBRUARY 24, 2023

Organizations can unite their siloed data and securely share governed data while executing diverse analytic workloads. Snowflake’s engine provides a solution for data warehousing, data lakes, data engineering, data science, data application development, and data sharing.

Analytics 52

Analytics Analytics Database Python 52

IBM Journey to AI blog

JULY 17, 2023

Within watsonx.ai, users can take advantage of open-source frameworks like PyTorch, TensorFlow and scikit-learn alongside IBM’s entire machine learning and data science toolkit and its ecosystem tools for code-based and visual data science capabilities.

AI 75

AI AI Machine Learning Machine Learning 75

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 303

Azure Analytics Analytics Data Lakes 303

Mlearning.ai

JULY 10, 2023

Without partitioning, daily data activities will cost your company a fortune and a moment will come where the cost advantage of GCP BigQuery becomes questionable. To create a Scheduled Query, the initial step is to ensure your SQL is accurately entered in the Query Editor.

SQL 52

SQL Database Database Administration Data Lakes 52

Alation

JULY 19, 2022

Increasingly, data catalogs not only provide the location of data assets, but also the means to retrieve them. A data scientist can use the catalog to identify several assets that might be relevant to their question and then use a built-in SQL query editor, like Compose, to access the data.

Data Scientist 59

Data Scientist Data Quality Data Science Data Analyst 59

IBM Journey to AI blog

MARCH 14, 2024

With newfound support for open formats such as Parquet and Apache Iceberg, Netezza enables data engineers, data scientists and data analysts to share data and run complex workloads without duplicating or performing additional ETL.

AWS 97

AWS Database AI AI 97

phData

DECEMBER 4, 2023

If you answer “yes” to any of these questions, you will need cloud storage, such as Amazon AWS’s S3, Azure Data Lake Storage or GCP’s Google Storage. Copy Into When loading data into Snowflake, the very first and most important rule to follow is: do not load data with SQL inserts!

Clustering 98

Clustering Database SQL Data Pipeline 98

phData

SEPTEMBER 27, 2024

Some modern CDPs are starting to incorporate these concepts, allowing for more flexible and evolving customer data models. It also requires a shift in how we query our customer data. Instead of simple SQL queries, we often need to use more complex temporal query languages or rely on derived views for simpler querying.

Data Models 52

Data Models Data Modeling Apache Kafka Data Lakes 52

phData

MAY 6, 2024

Another benefit of deterministic matching is that the process to build these identities is relatively simple, and tools your teams might already use, like SQL and dbt , can efficiently manage this process within your cloud data warehouse. Store this data in a customer data platform or data lake.

Data Lakes 52

Data Lakes Data Warehouse SQL Data Quality 52

The MLOps Blog

JUNE 27, 2023

Alignment to other tools in the organization’s tech stack Consider how well the MLOps tool integrates with your existing tools and workflows, such as data sources, data engineering platforms, code repositories, CI/CD pipelines, monitoring systems, etc. This provides end-to-end support for data engineering and MLOps workflows.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

phData

FEBRUARY 14, 2023

Qlik Replicate Qlik Replicate is a data integration tool that supports a wide range of source and target endpoints with configuration and automation capabilities that can give your organization easy, high-performance access to the latest and most accurate data. Why not just use one of the native ingestion methods for Snowflake?

Data Warehouse 59

Data Warehouse Azure AWS Database 59

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. Sathish and I met in 2004 when we were working for Oracle.

Data Governance 52

Data Governance Database Data Quality Data Lakes 52

AWS Machine Learning Blog

JANUARY 26, 2024

Define strict data ingress and egress rules to help protect against manipulation and exfiltration using VPCs with AWS Network Firewall policies. With Security Lake, you can get a more complete understanding of your security data across your entire organization.

AWS 138

AWS AI AI ML 138