Devin Parekh has co-led investment firm Insight Partners for 26 years. The firm manages $90 billion in assets. Unlike many venture capital investors, it rarely draws attention with bold statements, instead focusing on the performance of companies in its portfolio. As reported by Techcrunch Insight Partners’ strategy differs from that of funds effectively making their biggest bets on OpenAI and Anthropic. The firm has invested in both labs and participated in numerous funding rounds for Databricks, but it has not abandoned broad diversification. Over the long term, venture capital has always rewarded diversification. We are already on our 13th fund, so we have to think not in terms of one fund, but ten. – Devin Parekh Why Insight Partners Diversifies Its Investments Capital allocation across early-stage startups, scaling companies, buyout deals, and secondary sales does not follow a fixed ratio. As Parekh explained, the portfolio’s structure changes along with market conditions. Buyouts have become more difficult because of high interest rates, weak demand for debt financing for software companies, and falling valuations. Insight Partners has not completed any major deals of this kind since 2024. At the same time, venture capital valuations are rising again at rates reminiscent of 2021...

Fragmented Azure stackDriver Data across Cosmos DB, Synapse, Azure Data Lake, Functions, Power BI From the article 2 mentionsThe UK smart charger maker moved from a fragmented Azure stack to a single governed platform, according to Databricks. 80‑90% cost reductionOutcome EV data costs cut by 80‑90% after consolidating Cosmos DB and Synapse From the article 3 mentionsThe Synapse to Databricks migration delivered 80-90% business cost savings and 90% storage cost savings. drives adoption Databricks Unity CatalogCore Governed platform centralizing data and lineage across former Azure services From the article 9+ mentionsIndra Renewable Technologies cut EV charging data sprawl by consolidating on Databricks, and Databricks Unity Catalog governance sits at the center of that shift. powers self‑service Self‑service reportingEffect Business teams now get routine numbers without waiting on data teams Fragmented Azure stackDriver Data across Cosmos DB, Synapse, Azure Data Lake, Functions, Power BI From the article 2 mentionsThe UK smart charger maker moved from a fragmented Azure stack to a single governed platform, according to Databricks. Prototype migrationContext From the article 4 mentionsIndra started with a parallel prototype migration from Synapse to Databricks. Databricks Unity CatalogCore Governed platform centralizing data and lineage across former Azure services From the article 9+...

Databricks announced Wednesday that it will acquire database management platform Neon in a deal reportedly valued at around $1 billion, one of 13 known acquisitions to date for the San…

Data and AI tech giant Databricks has announced plans to invest more than $850m (£644m) in the UK to support its expansion in the country.

Databricks has announced plans to train 100,000 people in the UK over the next three years in generative AI and associated skills.

**Position: Databricks Architect with AWS, Java& Rest API exp** **Location: NYC – Onsite** **Duration: Long Term Contract** **Experience: 13-18 Years** As an Integration Architect with over 14+ years of experience, you will lead the design, development, and implementation of highly available, scalable, and low-latency integrated systems. You will collaborate closely with engineering teams and stakeholders to deliver modern integration solutions, focusing on both cloud platforms (Databricks, AWS) and on-premises environments. Your hands-on approach and technical leadership will help shape our journey towards robust, future-ready integrated systems. ## Key Responsibilities · Lead technical architecture, design, and delivery of high-performance, highly available integrated systems, including low-latency trading and backend platforms. · **Re-architect and optimize Databricks platforms, ensuring efficient integration with existing and new services.** · **Design and develop sophisticated REST API platforms to enable comprehensive system integration.** · Demonstrate expertise in Messaging Platforms (AMQ, Kafka, etc.) to facilitate effective inter-system communication and build integrated asynchronous platforms. · Utilise deep knowledge of No SQL, SQL, and stored procedures (Postgres, MySQL) in the context of integrated environments. · Assess health and performance of integrated software applications and databases, providing robust technology solutions for integration challenges. · Collaborate with Operations and Business teams to support and enhance trading, backend, and asset management systems with strong integration capabilities. · Analyse service and application performance using various programming languages, tools, and integration-focused techniques. · **Develop and maintain automated testing frameworks and CI/CD pipelines tailored for integrated systems.** · Provide technical leadership and mentorship to engineering teams, particularly in system integration best practices. · Guide non-technical staff in the effective use of integrated software and hardware systems. · Support applications with a strong understanding of system integration, test planning, scripting, and troubleshooting. ## Required Qualifications & Experience · Bachelor’s degree in Computer Science or related field, or equivalent experience. · **Minimum 10 years of experience in Software Engineering with a focus on system integration (Java, Python, GoLang).** · **At least 5 years in fintech, preferably in Capital Markets, Trade Lifecycle, Equities, Mutual Funds, Wealth Management, Payments, or SaaS, with demonstrable integration skills.** · Hands-on experience re-architecting Databricks platforms and managing related integrations. · Proficiency in Test Driven Development and writing effective unit/integration tests for integrated systems. · Strong understanding of CI/CD and automated testing frameworks for integrated environments (Jenkins, Git, Jira). · Expertise in Unix and Linux operating systems, including scripting to extract and transform data across integrated systems. · Familiarity with Scrum and Agile processes tailored for integration projects. · Experience with 24×7 highly available integrated architectures. · Excellent communication skills, with the ability to explain complex integration concepts to diverse audiences. · Strong analytical and problem-solving skills, with a focus on integration challenges. ## Nice to Have · Experience with Amazon Web Services (AWS) in integrated environments. · Advanced scripting skills on Linux for data extraction and transformation. ** ** **Regards** Naresh Kumar Jettem Njettem@tror.ai : : : : **APPLY NOW** 🔔 Get our daily C2C jobs / Hotlist notifications on WHATSAPP TELEGRAM LINKEDIN **** ### _Related_ Table of Contents Toggle * Key Responsibilities * Required Qualifications & Experience * Nice to Have * Related

We’re excited to announce that you can now orchestrate Azure Databricks Jobs from your Microsoft Fabric data pipelines! Databrick Jobs allow you to schedule and orchestrate a task or multiple tasks...

We’re excited to announce that Azure Data Factory now supports the orchestration of Databricks Jobs! Databrick Jobs allow you to schedule and orchestrate a...

書くのを少し迷いましたが、ユースケースとしては有用だと思いましたのでこちらにまとめます。 大量のテキストに対してLLMをバッチ処理で適用して、効率改善などビジネス上の課題を解決します。 テキストが大量にある 処理内容がLLMに適している(要約、情報抽出、翻訳、内容チェックなど) Databricksの日本語マニュアルは英語からの機械翻訳がベースとなっています。私を含めて日本人のエンジニア数名でチェックはしているのですが、どうしてもチェック漏れが発生していました。以下の画像のように、英単語があると語順がおかしくなるケースが散見されていました。 新たに翻訳されたページの連絡はもらっているものの、すべてのページの目検をやり続けるには限界があります。 マニュアルページの一覧はサイトマップとして公開されているので、これをクローリングしてDeltaテーブル化します。このテーブルに対してLLMバッチ推論で一括でチェックするようにしました。 プロンプトは以下の通りです。判定結果だけでなく、理由も出すように指示しています。 あなたは日本語が流暢なDatabricksの専門家です。右のDatabrickマニュアルを確認します。日本語としておかしい箇所、特に語順の間違いに注意してください。「SDKDatabricksOAuth」のような空白を含まない英語の羅列に注意してください。語順がおかしい可能性が高いです。固有名詞が英語のままであることは許容しますが、一般用語の翻訳漏れにも注意してください。全体的に日本語としての品質が低い場合にはNG、問題がなければOKを返してください: 判定結果はダッシュボードで確認できるようにしました。 サービングエンドポイントの準備 LLMバッチ推論では高いスループットが必要になるので、Provisioned Throughput(毎回翻訳を悩むのですが、スループット配備済みとかなんですかね。要ははスループット保証型です)のモデルサービングエンドポイントを使っています。モデルはLlama 3.1 405b instructです。 ノートブック %pip install bs4 lxml %restart_python import requests from bs4 import BeautifulSoup def extract_title_and_contents(url): response = requests.get(url) soup = BeautifulSoup(response.content.decode("utf-8", "ignore"), 'html.parser') # 更新時刻 last_modified = soup.find('time').get_text() # タイトル title = soup.find('title').get_text() # 本文 contents = soup.find('div', class_="theme-doc-markdown markdown").get_text() return title, contents, last_modified …

We’re excited to announce that you can now orchestrate Azure Databricks Jobs from your Microsoft Fabric data pipelines! Databrick Jobs allow you to schedule and orchestrate a task or multiple tasks in a workflow in your Databricks workspace. Since any operation in Databricks can be a task, this means you can now run anything in … <p class="link-more"><a href="https://blog.fabric.microsoft.com/en-us/blog/orchestrate-your-databricks-jobs-with-fabric-data-pipelines/" class="more-link">Continue reading<span class="screen-reader-text"> “Orchestrate your Databricks Jobs with Fabric Data pipelines”</span></a>