avatarYagmur Sahin

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zations-for-columnstore-compression">here</a>.</p><p id="fc40">An example of over-configured workload management is when resources are reserved for a workload group even when there are no active requests in it. When setting up a workload group, you can define the minimum % of resources that are always reserved for the group. This is very useful in cases when you have to ensure that SLAs are met, but it should always be done with caution. If such strict isolation of resources is not required, it’s better to use a shared resource pool in combination with different workload importances.</p><h2 id="3041">3. Using clustered columnstore index for staging</h2><p id="23d4">By default, dedicated SQL pool will set up tables using clustered columnstore indexes. These are highly compressed, column-based data structures optimized for analytical workloads on large tables, but they can be expensive to build.</p><p id="5dbe">When loading data into a clustered columnstore index, the rows are first split into row groups (batches) which then are separated into column segments before each of these segments get compressed.</p><figure id="c79e"><img src="https://cdn-images-1.readmedium.com/v2/resize:fit:800/1*NUSN2jBohgHVlSGhjbgToA.png"><figcaption><a href="https://learn.microsoft.com/en-us/sql/relational-databases/indexes/columnstore-indexes-data-loading-guidance?view=sql-server-ver16">Source</a></figcaption></figure><p id="0b0b">Because building a clustered columnstore index is a resource intensive process, using them for temporary staging tables creates an unnecessary overhead without providing any benefits. When comparing the throughput of bulk loading processes for clustered columnstore indexes and heap tables, a 2–3x difference can be observed (<a href="https://techcommunity.microsoft.com/t5/sql-server-blog/data-loading-performance-considerations-with-clustered/ba-p/305223">link</a>).</p><p id="1bcf">While avoiding the aforementioned 3 mistakes when working with a Synapse dedicated SQL pool can help you save cost by utilizing your resources more efficiently, finding the right combination of tools for your business needs can be challenging. We at Starschema can help you identify the technologies that will best serve your use cases and fine-tune them for optimal performance. <a href="https://starschema.com/contact">Reach out</a> — we’d love to talk.</p><p id="ed30">To learn how Synapse fares against Databricks, Snowflake, Redshift and BigQuery fare and scale in terms of query performance, cost per performance and differentiating feature value, see the results of our extensive testing in this white paper:</p><div id="2298" class="link-block"> <a href="https://starschema.com/kb/cloud-data-warehouse-benchmark-2023"> <div> <div> <h2>Cloud Data Warehouse Benchmark 2023</h2> <div><h3>As cloud data warehouse providers race to improve the scalability, performance and cost-effectiveness of their…</h3></div> <div><p>starschema.com</p></div> </div> <div> <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/0*QBnOLQvJ3CUAhQ_C)"></div> </div> </div> </a> </div><p id="28bd"><b>About the author</b></p><p id="a1e9"><i>Marton is a highly skilled data professional with experience in commerci

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al aviation and electronics manufacturing. He holds a Master’s degree in data science from Tilburg University and previously worked as a data scientist with the top management of a major European airline, where his transformational work helped save millions of euros for the company. In his current work as a data engineer at Starschema, he helps Fortune 500 companies build data platforms to unlock greater value from their data. Connect with Marton on <a href="http://www.linkedin.com/in/martonmesz">LinkedIn</a>.</i></p><p id="272c"><b>REACH OUT TO STARSCHEMA <a href="https://starschema.com/contact">HERE</a>:</b></p><div id="2f2e" class="link-block"> <a href="https://starschema.com/contact"> <div> <div> <h2>Let’s talk | Starschema</h2> <div><h3>We help your organization become data-driven</h3></div> <div><p>starschema.com</p></div> </div> <div> <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/0*JnBf22UiwDSON2-d)"></div> </div> </div> </a> </div><p id="742c"><b>READ MORE STORIES FROM STARSCHEMA:</b></p><div id="4934" class="link-block"> <a href="https://readmedium.com/find-the-balance-between-cloud-cost-and-efficiency-889958c39908"> <div> <div> <h2>Find the Balance between Cloud Cost and Efficiency</h2> <div><h3>Learn how to measure the ROI of a cloud migration and get clarity on the opportunities and challenges inherent in…</h3></div> <div><p>medium.com</p></div> </div> <div> <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/0*3onHo1F--Lp3oe9M)"></div> </div> </div> </a> </div><div id="a8c0" class="link-block"> <a href="https://readmedium.com/data-fabric-vs-data-mesh-find-the-right-fit-for-your-organization-40b37f4efba"> <div> <div> <h2>Data Fabric vs Data Mesh: Find the Right Fit for Your Organization</h2> <div><h3>Learn the differences between data mesh and data fabric architectures and find the right one for your data governance…</h3></div> <div><p>medium.com</p></div> </div> <div> <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/0*uR7nzrNVrfSbaH6x)"></div> </div> </div> </a> </div><div id="5263" class="link-block"> <a href="https://readmedium.com/from-guesswork-to-genius-how-to-get-maximum-value-from-marketing-data-and-automation-b7600346dee7"> <div> <div> <h2>From Guesswork to Genius: How to Get Maximum Value from Marketing Data and Automation</h2> <div><h3>See how one company used marketing data to learn more about their audience and run more effective campaigns — and how…</h3></div> <div><p>medium.com</p></div> </div> <div> <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/1*nJFWer2IZmGd2KJAGBR-3g.jpeg)"></div> </div> </div> </a> </div></article></body>

“Data” as an Object of Contracts/Within the Framework of Competition Law-1

It’s going to be a brief story that explains Competition Law itself, practice areas of competition law, the historic lineage of competition law and the “data” as a product of the free-market economy.

*This article will be divided into two articles to improve readability and due to the separate but combined nature of the topics.

In this article: “Why can data as a market product could be linked to competition law?” “What is competition law?” and “Brief history of competition law” will be discussed.

I chose this subject to underline how economics and law are two intertwined and closely related concepts and show that how "processable data" is a product in the market and that it is the subject of competition law as well as the subject of IT law. We are currently talking about which data is processed under which conditions. Ensuring privacy and security is one of our main goals. However, there is an unnoticed danger here. Due to the abundance of data, namely "big data," informs companies about how they can improve their products and earn money from you. They are important because the data is more than one person's personal data when it enters the market. As a market product, "Data" is more valuable than any other product on the market. So, if this data is collected in the hands of one person, wouldn't it be that one person's hands, is the “free-market” itself? Wouldn’t that person be the big market economy force in the “free market”?

Free-market economy is one of Europe’s fundamental principles. It allows businesses to thrive and provides consumers with a variety of choices. To gain customers, firms arrange their prices or offer higher quality products to be successful.

Development Competition Law in North America

After the Civil War in the US, there were major developments in both transportation and communications. With these revolutions, it became possible to sell products to other locations. The world became much smaller, and firms could able to grow faster and effectively. As a natural result, competition between firms and their products grew. With these revolutions in the 19th century, business enterprises intergraded in economic systems.

As a result of these transformative developments in North America in the late 19th century, large integrated national firms began to push smaller firms out of the game because they could still make big productions by making price reductions.

Consequently, smaller firms couldn’t compete with them fairly. The problem was simple: Big business enterprises could have been able to control both economic and political power. This affected social structure and legislative framework both in Canada and the US. After that, a lot of countries set their own rules about competition. With these big monopolies, the perceived threat to democracy and the free market these trusts represented led to the passage of the Sherman and Clayton Acts.

The Sherman Act authorized the Federal Government to institute proceedings against trusts in order to dissolve them. The purpose of the Sherman Act was not to protect competitors from harm from legitimately successful businesses, nor to prevent businesses from gaining honest profits from consumers, but rather to preserve a competitive marketplace to protect consumers from abuses.

After all these developments, competition policy grew and global compliance became the norm. For all of us, this meant that we were truly interconnected in a global community with this new form of economic organization and regulation.

Follow-up:

Competition Law
Big Data
Data Privacy
Data Governance
Databulls
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