IBM Journey to AI blog

SEPTEMBER 11, 2023

Codd published his famous paper “ A Relational Model of Data for Large Shared Data Banks.” Boyce to create Structured Query Language (SQL). enhances data management through automated insights generation, self-tuning performance optimization and predictive analytics. Chamberlin and Raymond F.

Database 101

Database SQL Data Warehouse Machine Learning 101

Smart Data Collective

JULY 20, 2021

These software tools rely on sophisticated big data algorithms and allow companies to boost their sales, business productivity and customer retention. 10 Panoply: In the world of CRM technology, Panoply is a data warehouse build that automates data collection, query optimization and storage management.

Big Data 142

Big Data Big Data ETL Analytics 142

Dataconomy

SEPTEMBER 27, 2023

The Microsoft Certified Solutions Associate and Microsoft Certified Solutions Expert certifications cover a wide range of topics related to Microsoft’s technology suite, including Windows operating systems, Azure cloud computing, Office productivity software, Visual Studio programming tools, and SQL Server databases.

Database Administration 113

Database Administration Database Azure Cloud Computing 113

IBM Journey to AI blog

SEPTEMBER 19, 2023

Business users will also perform data analytics within business intelligence (BI) platforms for insight into current market conditions or probable decision-making outcomes. Many functions of data analytics—such as making predictions—are built on machine learning algorithms and models that are developed by data scientists.

Data Science 94

Data Science Analytics Analytics Data Scientist 94

Mlearning.ai

FEBRUARY 16, 2023

The ultimate need for vast storage spaces manifests in data warehouses: specialized systems that aggregate data coming from numerous sources for centralized management and consistency. In this article, you’ll discover what a Snowflake data warehouse is, its pros and cons, and how to employ it efficiently.

Data Warehouse 52

Data Warehouse Business Intelligence Business Intelligence Database 52

AWS Machine Learning Blog

OCTOBER 9, 2024

Using Amazon CloudWatch for anomaly detection Amazon CloudWatch supports creating anomaly detectors on specific Amazon CloudWatch Log Groups by applying statistical and ML algorithms to CloudWatch metrics. Use AWS Glue Data Quality to understand the anomaly and provide feedback to tune the ML model for accurate detection.

AWS 94

AWS ML ML Data Quality 94

IBM Journey to AI blog

SEPTEMBER 25, 2023

This blog takes you on a journey into the world of Uber’s analytics and the critical role that Presto, the open source SQL query engine, plays in driving their success. Presto was able to achieve this level of scalability by completely separating analytical compute from data storage. What is Presto?

Data Lakes 102

Data Lakes Analytics Analytics Clustering 102

phData

SEPTEMBER 19, 2023

A rigid data model such as Kimball or Data Vault would ruin this flexibility and essentially transform your data lake into a data warehouse. However, some flexible data modeling techniques can be used to allow for some organization while maintaining the ease of new data additions.

Data Lakes 52

Data Lakes Data Modeling Data Models Data Warehouse 52

phData

MAY 12, 2023

KNIME Analytics Platform is an open-source, user-friendly software enabling users to create data science applications and services intuitively, without coding knowledge. Its visual interface allows you to design workflows, handle data extraction and transformation, and apply statistical methods or machine learning algorithms.

Data Warehouse 72

Data Warehouse Machine Learning Machine Learning Database 72

Dataconomy

APRIL 27, 2023

They are also designed to handle concurrent access by multiple users and applications, while ensuring data integrity and transactional consistency. Examples of OLTP databases include Oracle Database, Microsoft SQL Server, and MySQL. An OLAP database may also be organized as a data warehouse.

Database 159

Database Data Scientist Data Mining Data Mining 159

phData

NOVEMBER 28, 2024

“ Vector Databases are completely different from your cloud data warehouse.” – You might have heard that statement if you are involved in creating vector embeddings for your RAG-based Gen AI applications. in a 2D space based on the machine learning algorithm used. The below flow diagram illustrates this process.

Python 52

Python Database SQL Machine Learning 52

IBM Journey to AI blog

JULY 6, 2023

Just as humans can learn through experience rather than merely following instructions, machines can learn by applying tools to data analysis. Machine learning works on a known problem with tools and techniques, creating algorithms that let a machine learn from data through experience and with minimal human intervention.

Machine Learning 70

Machine Learning Machine Learning Data Science Big Data 70

Pickl AI

OCTOBER 17, 2024

Introduction ETL plays a crucial role in Data Management. This process enables organisations to gather data from various sources, transform it into a usable format, and load it into data warehouses or databases for analysis. Loading The transformed data is loaded into the target destination, such as a data warehouse.

ETL 52

ETL Data Warehouse Data Quality Data Governance 52

phData

JUNE 18, 2024

How to Prepare Data for Use in Machine Learning Models Data Collection The first step is to collect all the data you believe the model will need and ingest it into a centralized location, such as a data warehouse. We need to format it to be suitable for machine learning algorithms.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Pickl AI

APRIL 6, 2023

Having a solid understanding of ML principles and practical knowledge of statistics, algorithms, and mathematics. Having experience using at least one end-to-end Azure data lake project. Hands-on experience working with SQLDW and SQL-DB. Knowledge in using Azure Data Factory Volume. What is Polybase?

Azure 52

Azure Data Engineer Data Engineering Data Engineering 52

Pickl AI

APRIL 26, 2024

This comprehensive blog outlines vital aspects of Data Analyst interviews, offering insights into technical, behavioural, and industry-specific questions. It covers essential topics such as SQL queries, data visualization, statistical analysis, machine learning concepts, and data manipulation techniques.

Data Analyst 52

Data Analyst Data Analysis Data Analysis Machine Learning 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. Role of Data Scientists Data Scientists are the architects of data analysis.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Pickl AI

AUGUST 9, 2024

Understanding the differences between SQL and NoSQL databases is crucial for students. Data Warehousing Solutions Tools like Amazon Redshift, Google BigQuery, and Snowflake enable organisations to store and analyse large volumes of data efficiently. Students should learn how to apply machine learning models to Big Data.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Pickl AI

JULY 3, 2023

Here are steps you can follow to pursue a career as a BI Developer: Acquire a solid foundation in data and analytics: Start by building a strong understanding of data concepts, relational databases, SQL (Structured Query Language), and data modeling.

Business Intelligence 52

Business Intelligence Business Intelligence SQL Data Visualization 52

AWS Machine Learning Blog

JUNE 5, 2023

Data is the foundation for machine learning (ML) algorithms. One of the most common formats for storing large amounts of data is Apache Parquet due to its compact and highly efficient format. Athena allows applications to use standard SQL to query massive amounts of data on an S3 data lake.

Machine Learning 98

Machine Learning Machine Learning AWS Data Lakes 98

Women in Big Data

SEPTEMBER 7, 2024

The real advantage of big data lies not just in the sheer quantity of information but in the ability to process it in real-time. Variety Data comes in a myriad of formats including text, images, videos, and more. Veracity Veracity relates to the accuracy and trustworthiness of the data.

Big Data 59

Big Data Big Data Database Machine Learning 59

Pickl AI

JANUARY 28, 2025

This involves several key processes: Extract, Transform, Load (ETL): The ETL process extracts data from different sources, transforms it into a suitable format by cleaning and enriching it, and then loads it into a data warehouse or data lake. Data Lakes: These store raw, unprocessed data in its original format.

Business Intelligence 52

Business Intelligence Business Intelligence ETL Data Lakes 52

Pickl AI

SEPTEMBER 5, 2024

Data Preparation: Cleaning, transforming, and preparing data for analysis and modelling. Algorithm Development: Crafting algorithms to solve complex business problems and optimise processes. Collaborating with Teams: Working with data engineers, analysts, and stakeholders to ensure data solutions meet business needs.

Azure 52

Azure Data Scientist Data Science Machine Learning 52

Pickl AI

JULY 14, 2024

Handling Data Storage, Retrieval, and Management DBMS systems employ sophisticated algorithms to manage data storage efficiently. They allocate storage space dynamically, optimising performance and ensuring data integrity. Read Blogs: Differences Between SQL and T-SQL [with Example]. What is a Key in DBMS?

Database 52

Database SQL Data Warehouse Data Analyst 52

AWS Machine Learning Blog

FEBRUARY 28, 2023

Query the data using Athena By running Athena SQL queries directly on Amazon HealthLake, we are able to select only those fields that are not personally identifying; for example, not selecting name and patient ID, and reducing birthdate to birth year. In this post, we used Amazon S3 as the input data source for SageMaker Canvas.

ML 100

ML ML AWS Machine Learning 100

Alation

JULY 19, 2022

For these reasons, finding and evaluating data is often time-consuming. Instead of spending most of their time leveraging their unique skillsets and algorithmic knowledge, data scientists are stuck sorting through data sets, trying to determine what’s trustworthy and how best to use that data for their own goals.

Data Scientist 59

Data Scientist Data Quality Data Science Data Analyst 59

Pickl AI

JULY 8, 2024

Organisations leverage diverse methods to gather data, including: Direct Data Capture: Real-time collection from sensors, devices, or web services. Database Extraction: Retrieval from structured databases using query languages like SQL. NoSQL Databases: Flexible, scalable solutions for unstructured or semi-structured data.

Data Pipeline 52

Data Pipeline Data Quality Database Apache Kafka 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. Uses secure protocols for data security.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59

phData

JUNE 26, 2024

This is a perfect use case for machine learning algorithms that predict metrics such as sales and product demand based on historical and environmental factors. Cleaning and preparing the data Raw data typically shouldn’t be used in machine learning models as it’ll throw off the prediction.

Machine Learning 52

Machine Learning Machine Learning Data Engineering Data Engineer 52

Alation

APRIL 14, 2022

Data pipeline orchestration. Support for languages and SQL. Moving/integrating data in the cloud/data exploration and quality assessment. An inference algorithm that informs the analyst with a ranked set of suggestions about the transformation. Collaboration and governance. Low-code, no-code operation. Scheduling.

Data Governance 59

Data Governance ML ML Cloud Data 59

Snorkel AI

JUNE 27, 2023

First, you generate predictions and you store them in a data warehouse. So we write a SQL definition. And then during prediction, we can use stream SQL to compute these SQL features. We should be able to continually train the model on fresh data. So we need to access fresh data.

AI 52

AI AI Data Warehouse Machine Learning 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. It thrives on patterns, combinations of data points, and statistical probabilities.

Data Lakes 52

Data Lakes Data Warehouse Cloud Data SQL 52

Pickl AI

DECEMBER 9, 2024

Hive is a data warehouse tool built on Hadoop that enables SQL-like querying to analyse large datasets. What is the Difference Between Structured and Unstructured Data? Structured data is organised in tabular formats like databases, while unstructured data, such as images or videos, lacks a predefined format.

Big Data 52

Big Data Big Data Hadoop Apache Kafka 52

Snorkel AI

JUNE 27, 2023

First, you generate predictions and you store them in a data warehouse. So we write a SQL definition. And then during prediction, we can use stream SQL to compute these SQL features. We should be able to continually train the model on fresh data. So we need to access fresh data.

AI 52

AI AI Data Warehouse Machine Learning 52

Snorkel AI

JUNE 27, 2023

First, you generate predictions and you store them in a data warehouse. So we write a SQL definition. And then during prediction, we can use stream SQL to compute these SQL features. We should be able to continually train the model on fresh data. So we need to access fresh data.

AI 52

AI AI Data Warehouse Machine Learning 52

phData

OCTOBER 28, 2024

Understanding Matillion and Snowflake, the Python Component, and Why it is Used Matillion is a SaaS-based data integration platform that can be hosted in AWS, Azure, or GCP and supports multiple cloud data warehouses. Matillion supports writing code in Python, Bash Script, and native ANSI SQL commands.

Python 52

Python ETL AWS Database 52

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 Modeling 52

Data Modeling Data Models Apache Kafka Data Lakes 52