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.

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Pickl AI

JULY 25, 2024

From extracting information from databases and spreadsheets to ingesting streaming data from IoT devices and social media platforms, It’s the foundation upon which data-driven initiatives are built. Batch Processing In this method, data is collected over a period and then processed in groups or batches.

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Apache Kafka Data Lakes Data Warehouse Data Quality 52

phData

APRIL 29, 2024

In our previous blog, Top 5 Fivetran Connectors for Financial Services , we explored Fivetran’s capabilities that address the data integration needs of the finance industry. Now, let’s cover the healthcare industry, which also has a surging demand for data and analytics, along with the underlying processes to make it happen.

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SQL Azure Data Warehouse Cloud Data 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. This will open the ML transforms page.

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ODSC - Open Data Science

JANUARY 18, 2024

If you are a data scientist, you may be wondering if you can transition into data engineering. The good news is that there are many skills that data scientists already have that are transferable to data engineering. In this blog post, we will discuss how you can become a data engineer if you are a data scientist.

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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.

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The MLOps Blog

JANUARY 23, 2023

These tools may have their own versioning system, which can be difficult to integrate with a broader data version control system. For instance, our data lake could contain a variety of relational and non-relational databases, files in different formats, and data stored using different cloud providers. DVC Git LFS neptune.ai

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ML ML Data Lakes Machine Learning 52

The MLOps Blog

MAY 17, 2023

However, efficient use of ETL pipelines in ML can help make their life much easier. This article explores the importance of ETL pipelines in machine learning, a hands-on example of building ETL pipelines with a popular tool, and suggests the best ways for data engineers to enhance and sustain their pipelines.

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Pickl AI

JULY 8, 2024

Summary: This blog explains how to build efficient data pipelines, detailing each step from data collection to final delivery. Introduction Data pipelines play a pivotal role in modern data architecture by seamlessly transporting and transforming raw data into valuable insights.

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Data Pipeline Data Quality Database Apache Kafka 52

AWS Machine Learning Blog

SEPTEMBER 1, 2023

These teams are as follows: Advanced analytics team (data lake and data mesh) – Data engineers are responsible for preparing and ingesting data from multiple sources, building ETL (extract, transform, and load) pipelines to curate and catalog the data, and prepare the necessary historical data for the ML use cases.

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Pickl AI

AUGUST 9, 2024

A well-structured syllabus for Big Data encompasses various aspects, including foundational concepts, technologies, data processing techniques, and real-world applications. This blog aims to provide a comprehensive overview of a typical Big Data syllabus, covering essential topics that aspiring data professionals should master.

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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. Data Warehousing concepts and knowledge should be strong.

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Pickl AI

JULY 6, 2023

Hive is a powerful data warehousing infrastructure that provides an interface for querying and analyzing large datasets stored in Hadoop. In this blog, we will explore the key aspects of Hive Hadoop. Hive is a data warehousing infrastructure built on top of Hadoop. Here comes the role of Hive in Hadoop. What is Hadoop ?

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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.

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Data Pipeline ETL SQL Data Quality 59

Alation

MARCH 22, 2022

As the latest iteration in this pursuit of high-quality data sharing, DataOps combines a range of disciplines. It synthesizes all we’ve learned about agile, data quality , and ETL/ELT. This produces end-to-end lineage so business and technology users alike can understand the state of a data lake and/or lake house.

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Pickl AI

JULY 30, 2024

With its user-friendly interface and robust architecture, NiFi simplifies the complexities of data integration, making it an essential component for modern data-driven enterprises. This blog delves into the fundamentals of Apache NiFi, its architecture, and how it can leverage for effective data flow management.

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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. Replication of calculated values is not supported during Change Processing.

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IBM Journey to AI blog

JANUARY 18, 2023

In another decade, the internet and mobile started the generate data of unforeseen volume, variety and velocity. It required a different data platform solution. Hence, Data Lake emerged, which handles unstructured and structured data with huge volume. Data lakehouse was created to solve these problems.

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phData

SEPTEMBER 28, 2023

Dataflows allow users to establish source connections and retrieve data, and subsequent data transformations can be conducted using the online Power Query Editor. In this blog, we will provide insights into the process of creating Dataflows and offer guidance on when to choose them to address real-world use cases effectively.

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Dataversity

OCTOBER 26, 2022

Until immortality is invented, we’ll have to settle for solving the same problem in data enablement. Actionable data lost to time. With incredible advances in data […]. In arguably the most iconic scene from Bladerunner, replicant Roy Batty describes his personal memories as “lost in time, like tears in rain.”

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Dataversity

DECEMBER 20, 2021

“Data is the currency of the future,” many experts have predicted. The 21st century has been characterized by the astounding amount of data we’ve gained access to. But what happens if this data isn’t properly stored? A data swamp begins to develop, and accessing that data becomes difficult and sometimes impossible.

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Data Lakes Data Warehouse ETL Database 98

Dataversity

SEPTEMBER 6, 2021

The rush to become data-driven is more heated, important, and pronounced than it has ever been. Businesses understand that if they continue to lead by guesswork and gut feeling, they’ll fall behind organizations that have come to recognize and utilize the power and potential of data. Click to learn more about author Mike Potter.

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Data Warehouse Data Lakes ETL Cloud Data 98

IBM Journey to AI blog

MARCH 14, 2024

Db2 Warehouse fully supports open formats such as Parquet, Avro, ORC and Iceberg table format to share data and extract new insights across teams without duplication or additional extract, transform, load (ETL). This allows you to scale all analytics and AI workloads across the enterprise with trusted data. 

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Pickl AI

JUNE 28, 2023

But, this data is often stored in disparate systems and formats. Here comes the role of Data Mining. Read this blog to know more about Data Integration in Data Mining, The process encompasses various techniques that help filter useful data from the resource. Thereby, improving data quality and consistency.

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AWS Machine Learning Blog

AUGUST 21, 2023

You can streamline the process of feature engineering and data preparation with SageMaker Data Wrangler and finish each stage of the data preparation workflow (including data selection, purification, exploration, visualization, and processing at scale) within a single visual interface.

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Dataversity

APRIL 1, 2021

In the data-driven world we live in today, the field of analytics has become increasingly important to remain competitive in business. In fact, a study by McKinsey Global Institute shows that data-driven organizations are 23 times more likely to outperform competitors in customer acquisition and nine times […].

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AWS Machine Learning Blog

JUNE 18, 2024

The solution addressed in this blog solves Afri-SET’s challenge and was ranked as the top 3 winning solutions. This post presents a solution that uses a generative artificial intelligence (AI) to standardize air quality data from low-cost sensors in Africa, specifically addressing the air quality data integration problem of low-cost sensors.

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AWS Machine Learning Blog

JUNE 25, 2024

The customer review analysis workflow consists of the following steps: A user uploads a file to dedicated data repository within your Amazon Simple Storage Service (Amazon S3) data lake, invoking the processing using AWS Step Functions. The Step Functions workflow starts.

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Dataversity

MARCH 17, 2021

I do not think it is an exaggeration to say data analytics has come into its own over the past decade or so. What started out as an attempt to extract business insights from transactional data in the ’90s and early 2000s has now transformed into an […]. The post Is Lakehouse Architecture a Grand Unification in Data Analytics?

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Analytics Analytics Data Lakes Data Warehouse 52

IBM Journey to AI blog

JULY 3, 2024

To power AI and analytics workloads across your transactional and purpose-built databases, you must ensure they can seamlessly integrate with an open data lakehouse architecture without duplication or additional extract, transform, load (ETL) processes.

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phData

FEBRUARY 5, 2024

In this blog, we’re going to answer these questions and more. Walking you through the biggest challenges we have found when migrating our customer’s data from a legacy system to Snowflake. You’re in luck because this blog is for anyone ready to move or thinking about moving to Snowflake who wants to know what’s in store for them.

SQL 52

SQL Database Data Quality Data Warehouse 52

IBM Journey to AI blog

JULY 1, 2024

Extraction, transformation and loading (ETL) tools dominated the data integration scene at the time, used primarily for data warehousing and business intelligence. Critical and quick bridges The demand for lineage extends far beyond dedicated systems such as the ETL example.

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IBM Journey to AI blog

JULY 17, 2023

In this blog, I will cover: What is watsonx.ai? sales conversation summaries, insurance coverage, meeting transcripts, contract information) Generate: Generate text content for a specific purpose, such as marketing campaigns, job descriptions, blogs or articles, and email drafting support. What capabilities are included in watsonx.ai?

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AI AI Machine Learning Machine Learning 75

The MLOps Blog

OCTOBER 20, 2023

This typically involves dealing with complexities such as ensuring secure and simple access to internal data warehouses, data lakes, and databases. While often ignored by data scientists, I believe mastering ETL is core and critical to guarantee the success of any machine learning project.

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SQL Database Data Scientist Python 52

phData

NOVEMBER 9, 2023

This blog was originally written by Keith Smith and updated for 2024 by Justin Delisi. Snowflake’s Data Cloud has emerged as a leader in cloud data warehousing. What is a Data Lake? A Data Lake is a location to store raw data that is in any format that an organization may produce or collect.

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Alation

FEBRUARY 20, 2020

Data lakes, while useful in helping you to capture all of your data, are only the first step in extracting the value of that data. Watch the complete video interview below: Subscribe to Alation's Blog. Get the latest data cataloging news and trends in your inbox.

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IBM Journey to AI blog

AUGUST 4, 2023

By leveraging data services and APIs, a data fabric can also pull together data from legacy systems, data lakes, data warehouses and SQL databases, providing a holistic view into business performance. Then, it applies these insights to automate and orchestrate the data lifecycle.

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