Database Tips: 7 Reasons Why Data Lakes Could Solve Your Problems

With the volume, velocity, and variety of today’s data, we have all started to acknowledge that there is no one-size-fits-all database for all data needs. Instead, many companies shifted towards choosing the right data store for a specific use case or project.

The distribution of data across different data stores brought the challenge of consolidating data for analytics. Historically, the only viable solution was to build a data warehouse: extract data from all those different sources, clean and bring it together, and finally, load this data to polished Data Warehouse (DWH) tables in a well-defined structure. While there is nothing wrong with this approach, a combination of a data lake and a data warehouse may be just the solution you need. Let’s investigate why.

1. Building a staging area for your data warehouse

2. Audit log of all data ever ingested into your data ecosystem thanks to the immutable staging area

3. Increase the time-to-value and time-to-insights

4. A single data platform for real-time and batch analytics

5. Costs

6. Convenience

7. Future proof

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Database Tips: 7 Reasons Why Data Lakes Could Solve Your Problems
Gerhard  Brink

Gerhard Brink

1620629020

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).


This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Database Vs Data Warehouse Vs Data Lake: A Simple Explanation

Databases store data in a structured form. The structure makes it possible to find and edit data. With their structured structure, databases are used for data management, data storage, data evaluation, and targeted processing of data.
In this sense, data is all information that is to be saved and later reused in various contexts. These can be date and time values, texts, addresses, numbers, but also pictures. The data should be able to be evaluated and processed later.

The amount of data the database could store is limited, so enterprise companies tend to use data warehouses, which are versions for huge streams of data.

#data-warehouse #data-lake #cloud-data-warehouse #what-is-aws-data-lake #data-science #data-analytics #database #big-data #web-monetization

Siphiwe  Nair

Siphiwe Nair

1625133780

SingleStore: The One Stop Shop For Everything Data

  • SingleStore works toward helping businesses embrace digital innovation by operationalising “all data through one platform for all the moments that matter”

The pandemic has brought a period of transformation across businesses globally, pushing data and analytics to the forefront of decision making. Starting from enabling advanced data-driven operations to creating intelligent workflows, enterprise leaders have been looking to transform every part of their organisation.

SingleStore is one of the leading companies in the world, offering a unified database to facilitate fast analytics for organisations looking to embrace diverse data and accelerate their innovations. It provides an SQL platform to help companies aggregate, manage, and use the vast trove of data distributed across silos in multiple clouds and on-premise environments.

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#featured #data analytics #data warehouse augmentation #database #database management #fast analytics #memsql #modern database #modernising data platforms #one stop shop for data #singlestore #singlestore data analytics #singlestore database #singlestore one stop shop for data #singlestore unified database #sql #sql database

Data Lakes Are Not Just For Big Data - DZone Big Data

We recently wrote an article debunking common myths about data lake architectures, data lake definitions, and data lake analytics. It is called "What is a Data Lake_? Get A Leg Up Avoiding The Biggest Myths." _In that article, we framed the current conversation about data lakes and how they fit within enterprise data strategies. This topic has historically been confusing and opaque for those wanting to get value from a data lake due to conflicting advice from consultants and vendors.

One area that can be particularly confusing is the perception that lakes are only for “big data.” If you spend any time reading materials on lakes, you would think there is only one type and it would look like the Capsian Sea (it’s a lake despite “sea” in the name). People describe data lakes as massive, all-encompassing entities, designed to hold all knowledge. The good news is that lakes are not just for “big data” and you have more opportunities than ever to have them be part of your data stack.

Yes, There Are Different Types of Data Lakes

Just as they do in nature, lakes come in all different shapes and sizes. Each has a natural state, often reflecting ecosystems of data, just like those in nature reflect ecosystems of fish, birds, or other organisms.

Unfortunately, the “big data” angle gives the impression that lakes are only for “Caspian” scale data endeavors. This certainly makes the use of data lakes intimidating. As a result, describing things in such massive terms makes the concept of a lake inaccessible to those who can benefit from them on a smaller scale. Here are a few data lake examples;

  • **The Great “Caspian”: ** Just like the Caspian is a large body of water, this type of lake is a large, broad repository-diverse set of data. This broad collection of diverse data reflects information from across the enterprise. This is how most data lake efforts are framed.
  • **Temporary “Ephemeral”: **Just like deserts can have small, temporary lakes, an Ephemeral exists for a short period of time. They may be used for a project, pilot, PoC or a point solution and they are turned off as quickly as they were turned on.
  • **Domain “Project”: **These lakes, like Ephemeral data lakes, are often focused on specific knowledge domains. However, unlike the Ephemeral lake, this lake will persist over time. These may also be “shallow,” meaning they may be focused on a narrow domain of data such as media, social, web analytics, email, or similar data sources.

We recently worked with a customer to create a “Domain” type lake. This lake would hold Adobe event data to an AWS to support an enterprise Oracle Cloud environment. Why AWS to Oracle? It was an efficient and cost-effective data consumption pattern for the customer Oracle BI environment, especially considering the agility and economics of using an AWS lake and Athena as the on-demand query service for lake content.

By design, all types of lakes should embrace an abstraction that minimizes risk and affords you greater flexibility. Also, they should be structured for easy consumption independent of their size. This ensures a lake used by a data scientist or business user or analyst all have an environment structured for easy data consumption.

Getting Started With Data Lakes

Being a successful early adopter means taking a business value approach rather than a technology one. Here are a few tips as you think about how to get started:

  • Focus: Seek opportunities where you can deploy an “Ephemeral” or “Project” solution. This will ensure you reduce risk and overcome technical and organizational challenges so your team can build confidence with lakes.
  • Passion: Make sure you have an “evangelist” or “advocate” internally, someone who is passionate about the solution and adoption within the company.
  • Simple: Embrace simplicity and agility, put people, processes, and technology choices through this lens. The lack of complexity should not be seen as a deficiency but a byproduct of thoughtful design.
  • Narrow: Keep the scope narrow and well defined by limiting your lake to understand data, say exports from ERP, CRM, Point-of-Sales, Marketing, or Advertising data. Data literacy at this stage will help you understand workflow around data structure, ingest, governance, quality, and testing.
  • Experiment: Pair your lake with a modern BI and analytics tools like Tableau, Power BI, Amazon Quicksight, or Looker. This will allow non-technical users an opportunity to experiment and explore data access via a lake. This allows you to engage a different user base that can assess performance bottlenecks, discover opportunities for improvements, possible linkages to any existing EDW systems (or other data systems), and additional candidate data sources.

#big data #data lake #data lakes #data lake architecture #data lake solutions #data analysis

Data Lake and Data Mesh Use Cases

As data mesh advocates come to suggest that the data mesh should replace the monolithic, centralized data lake, I wanted to check in with Dipti Borkar, co-founder and Chief Product Officer at Ahana. Dipti has been a tremendous resource for me over the years as she has held leadership positions at Couchbase, Kinetica, and Alluxio.

Definitions

  • A data lake is a concept consisting of a collection of storage instances of various data assets. These assets are stored in a near-exact, or even exact, copy of the resource format and in addition to the originating data stores.
  • A data mesh is a type of data platform architecture that embraces the ubiquity of data in the enterprise by leveraging a domain-oriented, self-serve design. Mesh is an abstraction layer that sits atop data sources and provides access.

According to Dipti, while data lakes and data mesh both have use cases they work well for, data mesh can’t replace the data lake unless all data sources are created equal — and for many, that’s not the case.

Data Sources

All data sources are not equal. There are different dimensions of data:

  • Amount of data being stored
  • Importance of the data
  • Type of data
  • Type of analysis to be supported
  • Longevity of the data being stored
  • Cost of managing and processing the data

Each data source has its purpose. Some are built for fast access for small amounts of data, some are meant for real transactions, some are meant for data that applications need, and some are meant for getting insights on large amounts of data.

AWS S3

Things changed when AWS commoditized the storage layer with the AWS S3 object-store 15 years ago. Given the ubiquity and affordability of S3 and other cloud storage, companies are moving most of this data to cloud object stores and building data lakes, where it can be analyzed in many different ways.

Because of the low cost, enterprises can store all of their data — enterprise, third-party, IoT, and streaming — into an S3 data lake. However, the data cannot be processed there. You need engines on top like Hive, Presto, and Spark to process it. Hadoop tried to do this with limited success. Presto and Spark have solved the SQL in S3 query problem.

#big data #big data analytics #data lake #data lake and data mesh #data lake #data mesh